Helix Technologies Ltd., a U.K.-based developer of high-performance, ceramic-based helix antennas, has secured funding that will enable continued development of antennas for a wide range of applications including autonomous vehicles, drones, internet of things and machine-to-machine communications.
Photo: HelixAntenia
The company closed its Phase B funding round with GBP 650,000 of financing provided by private investors.
The company said that the driverless car segment, both GNSS and vehicle-to-everything (V2X) dedicated short-range communications (DSRC) applications, represents the most immediate and compelling need and business opportunity for its helix antenna technology.
Helix Technologies said its dielectric-loaded helix antennas will provide significant performance advantages over incumbent antenna technologies for next-generation GNSS and V2X applications.
The use of a dielectric ceramic core gives its antennas unique properties including unsurpassed gain/efficiency per unit of volume and more effective and predictable behaviour in a wide range of challenging user scenarios.
“We are grateful for the support of our investors which allows us to develop innovative solutions for this exciting growth market,” said John Yates, managing director of Helix Technologies. “The first self-driving cars are widely forecast to be on the market between 2019 and 2021. Any navigation and communications equipment used onboard will have to fulfil the highest-possible standards on safety, integrity and accuracy.”
The company expects to have prototypes of its V2X DSRC antenna available by the second quarter of 2018 and its NEXTGEN GNSS antenna by the third quarter of 2018.
According to the company, the use of the ceramic core enables the fabrication of antennas that are physically smaller than conventional antennas, behave much more effectively and predictably in a wide range of challenging user scenarios and have many compelling technical advantages which include:
Maintaining radiation efficiency near absorbing objects (such as the human body)
Improving the accuracy of GNSS systems in multipath environments (such as in cities)
Operation in sub-optimal orientations towards the sky
Are able to be placed into very tightly integrated systems
Operation in slim devices without a ground plane
Unsurpassed gain/efficiency per unit of volume
Simple and robust design and construction for durability and reliability
In this month’s column, we review the history and future of software-defined radios (SDRs), looking in particular at GNSS SDRs.
This online version of the print article includes two bonus sections for which there wasn’t room in the magazine: New Frontiers: GNSS SDRs in Space and The Economics of SDRs.
By James T. Curran, Carles Fernández-Prades, Aiden Morrison and Michele Bavaro
Innovation Insights with Richard Langley
I had a fairly normal childhood—as a nerd. I was interested in radio and so was my sister. For her, it was the local AM radio stations where she could hear the latest Beatles’ hits on her six-transistor handheld portable. But for me, it was shortwave radio. I received a Knight-Kit two-tube regenerative shortwave receiver for Christmas 1963 when I was 14. It used one tube for the RF section and one tube for the audio amplifier. Using a random-length antenna above my mother’s clothesline, I was able to log radio stations from more than 100 countries during my high-school days.
With the pressures of university studies and starting to work for a living, I put my radio hobby on hold. But on an Air Canada flight to a conference early in 1985, I spotted an advertisement in the inflight magazine for the diminutive Sony ICF-7600D portable shortwave receiver — the height of miniaturization of microprocessor-controlled receivers at the time — and I acquired one in Hong Kong in May of that year before starting a lecture tour in the People’s Republic of China. I used the Sony receiver extensively at home and on trips overseas and heard many interesting broadcasts over the years including President Gorbachev’s resignation speech live from Radio Moscow.
Fast forward to 2013, when I purchased my first software-defined radio (SDR) receiver, a FUNcube Dongle Pro+, with frequency coverage from longwave up to the L-band. Interfaced via USB to a computer and bespoke software, an SDR receiver allows one to monitor a wide swath of the radio spectrum or record it for future analysis as in-phase and quadrature components. I have since acquired several other SDR receivers, and the capability of these units keeps getting better and better, delighting me and my fellow radio hobbyists. But these improvements in SDR technology extend to other uses of the radio spectrum including GNSS. In this month’s column, we review the history and future of SDRs looking in particular at GNSS SDRs. And what the Beatles said about improving one’s nature as a human being also aptly describes the performance of SDRs: it’s getting better all the time.
The software-defined radio (SDR) has an infinite number of interpretations depending on the context for which it is designed and used. By way of a starting definition, we choose to use that of a reconfigurable radio system whose characteristics are partially or fully defined via software or firmware. In various forms, the SDR has permeated a wide range of user groups, from military and business to academia and the hobby radio community.
SDR technology has evolved steadily over the decades following its birth in the mid-1980s, with various surges of activity being generally aligned with new developments in related technologies (processor power, serial busses, signal processing techniques and SDR chipsets). At present, it appears that we are experiencing one such surge, and the GNSS SDR is expanding in many directions. The proliferation of collaboration and code-sharing sites such as GitHub has enabled communities to share and co-develop receiver technology; the rise in the maker-culture and crowdsourcing has led to the availability of high-performance radio-frequency (RF) front ends; and the adoption of SDRs by some major telecommunications companies has led to the availability of suitable integrated circuits.
These contributing factors have played a part in an increased uptake of GNSS SDRs in military, scientific and commercial applications. In this article, we explore the recent trends and the technology behind them.
SDR TOPOLOGIES
The software-defined radio for GNSS has evolved over the past decade, both in terms of the adoption of new frequencies, new signals and new systems, as they have become available; as well as the adoption of new processing platforms and their associated processing techniques. Shown in FIGURE 1 is a (simplified) depiction of how the topology of the software-defined GNSS receiver has evolved over the years (a–d) with a hint at where it might go next (e, f).
FIGURE 1. A simplified depiction of different SDR topologies (GPP = general-purpose processor, GPU = graphics processing unit, FPGA = field-programmable gate array, SoC = system on chip, RFSoM = radio-frequency system on module, RFSoC = radio-frequency system on chip).
In a traditional GNSS SDR, as depicted in Figure 1 (a), the RF front end typically interfaces with the general-purpose processor (GPP) through a standard bus, and intermediate-frequency (IF) samples are streamed to a buffer. Once on the GPP, basic operations such as correlation, acquisition/tracking, measurement generation and positioning were performed.
Of all of the operations performed by a GNSS receiver, correlation is (by some orders of magnitude) the most computationally intensive. However, the correlation operations are relatively simple, often requiring only integer arithmetic, and can be easily parallelized. When running on modern processors, optimized software receivers can avail themselves of multi-threading (task parallelism) or the operations can be vectorized to exploit data parallelism (single-instruction, multiple data).
Beyond a certain number of GNSS signals and a certain bandwidth, a GPP simply cannot cope, and many SDR receivers looked to hardware acceleration for the correlation process. This either took the form of a graphics processing unit (GPU), or a field-programmable gate array (FPGA), as depicted in Figure 1(b), both of which are well suited to highly parallel tasks. These processing platforms can be powerful and efficient, and so can almost alleviate all challenges associated with correlation. This is not the only way to alleviate the processing burden, as it is also possible to delegate the correlation task to a network of computers. This “cloud” receiver architecture, depicted in Figure 1(e), has received particular attention of late, showing promise for certain niche applications. This computation-in-the-cloud trend has partially reverted with the proliferation of many-core desktop and mobile processors, but at a certain level of signal or processing complexity, the extensions remain applicable.
Nowadays, data throughput becomes an important consideration. When considering multi-constellation, multi-frequency receivers, the objective is often to preserve signal quality, which implies high bandwidth and high digitizer resolution. A triple-frequency front end might easily produce in excess of 100 or even 500 megabytes per second. When this data is delivered to the GPP or somewhere in the host computer, and then offloaded to the GPU (or any other hardware accelerator), it might be handled twice, exacerbating the bottleneck. To overcome this problem (and for other practical architectural reasons) it can be preferable to interface the front end directly with the accelerator, where correlation was performed, and leave the brains of the receiver (including loop closure; data processing; and position, velocity and time computation) on the GPP. This is a particularly convenient approach when using an FPGA accelerator, as shown in Figure 1(d).
A similar architecture can be achieved using modern system-on-chip (SoC) integrated circuits (ICs), which can offer a large FPGA and a powerful GPP on the same piece of silicon, as depicted in Figure 1 (d). Indeed, a number of receivers using this architecture have seen commercial and scientific success, having many of the benefits of dedicated silicon while retaining the benefits of the software-defined radio (for example, the Swift Navigation Piksi Multi GNSS Module). Recent developments in the field have seen the world’s first RF system-on-module (RFSoM) or system-on-chip (RFSoC) devices, targeting 5G mobile communications applications. With an architecture similar to that of Figure 1(f), the IC touts up to eight inputs and eight outputs (8×8) multiple input, multiple output (MIMO) with 12-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) running at rates of 2/4 gigasamples per second. Depending on how this trend evolves (assuming lighter versions become available), this might offer an exciting new platform for GNSS SDRs, simultaneously capable of multi-frequency and multi-antenna operation.
RF HARDWARE: THE ENABLER
GNSS SDRs see the world through a hardware peripheral, and the capability of this hardware defines the perimeter between what the receiver can and cannot do. In essence, the front-end peripheral converts one or more analog RF signals at the antenna to a stream or sequence of packets of digital-baseband/IF data to the GPP.
A software-defined radio for GNSS benefits greatly from being flanked in the RF spectrum on both sides by signals that are of interest to the civilian population. Applications such as Digital Video Broadcasting — Terrestrial (DVB-T) and Digital Video Broadcasting — Satellite Second Generation (DVB-S2) receivers have resulted in the availability of a wide range of low-cost RF ICs that are tunable to GNSS frequencies (typically spanning from 900 MHz to 2.1 GHz), which, along with dedicated GPS ICs, were at the heart of early GNSS SDR front ends. Later developments in ICs designed around the 2/3/4G mobile communications standards brought another generation of ICs, bringing higher instantaneous bandwidth, higher ADC resolution and MIMO, and re-transmit capability. With the increase in popularity of the software-defined radio for cognitive radio, Wi-Fi, 3G and Long-Term Evolution or LTE, and enjoying the benefits of a crowdfunding movement, a wide range of front-end peripherals quickly appeared. Many of these front ends are compatible with GNSS, offering significantly increased performance relative to their predecessors. A selection of some GNSS-compatible SDR peripherals (both new and old) is shown in TABLE 1.
TABLE 1. A selection of GNSS-compatible SDR front ends (Half duplex = transmit and receive but not simultaneously; Full duplex = transmit and receive simultaneously).
Reference Oscillators. Although many of the requirements of modern telecommunications ICs are beyond what is needed for GNSS (such as ADC resolution, frequency range, bandwidth and linearity), clock stability is often inadequate. Communications signals are generally received at high signal-to-noise ratio so the carrier can be easily recovered, even given very poor clock stability.
In contrast, clock stability can be critical for GNSS applications, due to the required comparatively long coherent integration period (greater than 1 millisecond) for a couple of reasons. Firstly, because the search-space granularity is related to the integration period and the size of the search space to the frequency uncertainty, clock accuracy is important, as an uncertainty of some tens of kHz might increase acquisition time. Secondly, the short-term stability is important as a large degree of phase wander can be challenging when attempting to track the carrier phase with a loop-update rate below 1 kHz. In fact, this issue was so pronounced on early RTL-SDR DVB-T front ends, that later revisions upgraded the quartz reference oscillator to a more respectable 0.5 parts per million temperature-compensated crystal oscillator (TCXO). Typically, a TCXO with an accuracy of better than 1 part per million is preferable, but this metric alone is far from sufficient.
Depending on the class of signals for which the SDR front end will be used, the characteristics of the oscillator, the configuration of its support electronics, and even whether the mixers and analog-to-digital conversion process use the same reference can vary. For example, not all TCXOs are suitable for GNSS applications due to the way in which they internally apply their temperature compensations. If a given TCXO uses a stepwise compensation configuration based on any form of digital feedback, the size of the resulting steps can severely impact the GNSS tracking loops. Even if a given TCXO has a suitable compensation curve and implementation, as well as low and acceptable intrinsic phase noise, every other link in the clock chain must preserve this performance. In some front-end implementations, swapping out a low-quality clock for a higher quality one is sufficient, but in others there can be design limitations in the oscillator power supply, the oscillator signal conditioning, subsequent clock generation steps, or distribution routing that can prevent the design from ever being suitable for GNSS use. This can be critical in cases where the carrier phase is of interest, for example, where phase coherence between channels is important for multi-frequency linear combinations, or for multi-antenna systems.
Fortunately, many modern SDR front ends support the use of an external clock. This feature can also be important when attempting to combine two front-end peripherals to effect a dual-frequency or dual-antenna software receiver.
The Bus. An intrinsic bottleneck for any SDR system is the fact that some form of connection or bus is needed to carry data from the collection point to the processing element. In a fully integrated system, this connection still exists, but it is typically a trace on a circuit board or even a pathway within an integrated device. In contrast, in an SDR this often takes the form of a cable or connector between the physically discrete system modules. In cases where the devices are discrete, it is often necessary to implement some data buffering on both ends of the bus.
The suitability of a particular bus is often determined by the sustained data throughput rate required by the application and, in some cases, the latency of the bus. An example of a number of interfaces popular in modern SDR front ends is shown in FIGURE 2, illustrating the nominal throughput and the minimum latency of each. In the case of a GNSS SDR, the minimum conceivable throughput required would be hundreds of megabytes per second, but a system could easily use in excess of 200 megabytes per second for multi-frequency, high-bit-depth data.
Of course, in post-processing applications, bus latency is not a factor. However, certain applications may require that this latency is small, or bounded, or somehow deterministic. Applications such as closed-loop vehicle control or certain safety systems might impose tight requirements on latency. High or unpredictable latency in GNSS measurements might lead to loop instability, in the case of a control system, or might erode safety margins. Although the trend in modern interfaces is for higher throughput, only certain interfaces offer low latency.
FIGURE 2. Bandwidth vs. latency scatter plot for popular buses.
The Silicon. In comparison with less-flexible fixed-function GNSS receiver chips, GNSS SDR hardware platforms provide the opportunity to exchange one to three orders of magnitude of power consumption and system size to gain substantial control over the characteristics of the design. Moreover, one of the other main differences between GNSS front ends and general purpose SDR front ends is the number of bits of ADC resolution and the conversion linearity. Both contribute to power consumption. However, it may be worth considering that GNSS-specific front ends have not received as much attention as telecommunications front ends and, consequently, there is at least a generational gap in silicon mask technology (most GNSS products are at the 350-nanometer level).
In terms of GNSS-specific devices, products such as the SiGe SE4110L, the Maxim MAX2769 and Saphyrion’s SM1027U provide a solution for slightly flexible L1 GPS, Galileo or, in some chip revisions, GLONASS operation. These kinds of chips support a few sampling rates and filtering configurations.
In the middle ground are the much more flexible chips from Maxim including the MAX2120 and MAX2112, which provide total L-band coverage, a myriad of filtering options, and adjustable gain control, all within a 0.3-watt power budget per channel (RF portion only). These chips allow for single-band coverage of adjacent GNSS signals such as GPS and GLONASS L1 or L2 in a single non-aliased RF band.
In terms of multi-channel options, devices such as the Maxim MAX19994A or the NTLab NT1065 offer dual- or quad-channel functionality, respectively. Similar functionality can be achieved by pairing downconversion and IF receiver ICs such as, for example, the Linear Technologies LTC5569 dual-active downconverting mixer and the Analog Devices AD6655 IF receiver, which might offer sufficient performance for high-accuracy dual-frequency positioning.
Higher up the cost, power and complexity structure are radios designed explicitly to support SDR applications that happen to cover GNSS bands such as the Lime LMS6002d/LMS7002M and the Analog Devices AD9364. Notably, these provide receive and transmit channels and frequency coverage up to 6 GHz.
Another interesting and relevant trend is in the use of direct RF sampling ICs, which offer the possibility of full L-band coverage and multi-antenna support. Examples include the Texas Instruments ADS54J40, which offers a dual-channel, 14-bit, 1.0-gigasamples-per-second ADC, or the LM97600 offering a 7.6 bit, quad-channel, 1.25-gigasamples-per-second ADC.
Future Trends, Limitations and Opportunities. Most of the innovation in SDR peripherals has taken place in the telecommunications domain. The GNSS SDR community, being comparatively small, has benefited from these innovations, insofar as they were applicable, but has had little influence over their design.
Looking at the bigger picture, it is clear that GNSS SDRs will simply have to follow the road paved by telecommunications SDRs. We will have to use what is made available, and so future trends in GNSS SDRs will likely be driven by the needs of the telecommunications SDR community.
So what are these trends and will they be aligned with GNSS trends? The answer seems to be yes and no. One of the bigger trends in modern GNSS receivers is the move to dual- or multi-frequency and a second trend is towards multi-antenna receivers for attitude determination or multi-element antennas for interference management. Meanwhile, telecommunications applications are almost universally using MIMO transceivers; however, they don’t seem to be using multiple (simultaneous) carriers.
What is particularly interesting is that the requirements for a MIMO transceiver are well aligned with that of a null-steering GNSS antenna: namely high linearity and high ADC resolution, and phase-coherence between channels (provided by, for example, the Lime Microsystems LMS7002M or the Analog Devices AD9361). As a result, it is possible (or even likely) that in the near future we will see more innovation in GNSS SDRs in the area of multi-antenna processing than in multi-frequency processing.
Signal Processing Techniques for SDRs. As mentioned above, signal correlation for acquisition and tracking is the most computationally intensive operation conducted by a GNSS receiver. In software receivers, many signal acquisition strategies are built around the fast Fourier transform (FFT) algorithm with a signal tracking rake of three or more correlators per signal. When targeting real-time processing, these operations need to be applied to a stream of signal samples arriving at a rate of many megasamples per second. This is a challenge for GPPs when implementing a multi-constellation, multi-frequency GNSS receiver.
The processing task can either be alleviated or accelerated. Assistance data can allow the receiver to reduce the size of the search acquisition space, thereby dramatically reducing the overall computational load. In many cases, the software receiver is running on a host computer with many connectivity options. Alternatively, a variety of options are available for accelerating the tasks.
Parallelization. The main approach for accelerating GNSS signal processing is parallelization. Shared-memory parallel computers can execute different instruction streams (or threads) on different processors, or by interleaving multiple instruction streams on a single processor (simultaneous multithreading or SMT), or both. This approach is referred to as task parallelism, and it is well supported by the main programming languages, compilers and operating systems. This approach fits naturally with the architecture of a GNSS receiver, which has many channels (one per satellite and frequency band) operating in parallel over the same input data. When programmed with the appropriate design, execution can be accelerated almost linearly with the number of processing cores. However, the spreading of processing tasks along different threads must be carefully designed in order to avoid bottlenecks (either in the processing or in memory access).
In combination with task parallelization, software-defined receivers can still resort to another form of parallelization: instructions that can be applied to multiple data elements at the same time, thus exploiting data parallelism. This computer architecture is known as Single Instruction Multiple Data (SIMD), where a single operation is executed in one step on a vector of data, as illustrated in FIGURE 3.
FIGURE 3. Illustration of the operation of single-instruction multiple-data (SIMD) processors, which take a multiple-data input (arguments) and produce multiple results, given a single instruction operated in parallel in a set of processing units (PUs).
In GNSS receivers, this type of instruction can implement operations like multiply-and-accumulate across multiple (16, 32, 64 and so on) samples in a single clock cycle. Intel introduced the first instance of 64-bit SIMD extensions, called MMX, in 1997. Later SIMD extensions, SSE 1 to 4, added multiple 128-bit registers. AMD quickly followed and SIMD is now present in almost all modern processors.
Later, Intel introduced more new instruction sets called Advanced Vector Extensions (AVX) featuring 256-bit registers, new instructions and a new coding scheme. In 2013, AVX-2 expanded most integer commands to 256 bits and by 2016, the introduction of AVX-512 provided 512-bit extensions. SIMD technology is also present in embedded systems: NEON technology is a 128-bit SIMD architecture extension for the ARMv7 Cortex-A series processors, providing 32 registers, 64-bits wide (dual view as 16 registers, 128-bits wide), and AArch64 NEON for ARMv8 processors, which provides 32 128-bit registers. In many cases, well written code will be automatically implemented as some combination of these SIMD intrinsics. In other cases, they can be coded explicitly.
Hardware Acceleration. Another possibility for accelerating signal processing is to offload computation-intensive portions of the workload to a device external to the main GPP executing the software. This is the case of graphics processing units (GPUs). Such processor architecture follows another parallel programming model called Single Instruction, Multiple Threads (SIMT). While in SIMD elements of short vectors are processed in parallel, and in SMT instructions of several threads are run in parallel, SIMT is a hybrid between vector processing and hardware threading. Currently, Open Computing Language or OpenCL is the most popular open GPU computing language that supports devices from several manufacturers, while CUDA (originally, Compute Unified Device Architecture) is the dominant proprietary framework specific for Nvidia GPUs. The key idea is to exploit the computation power of both GPP cores and GPU execution units in tandem for better utilization of available computing power. The main constraint in using GPUs is memory bandwidth. If not programmed carefully, most of the time will be spent on transferring data back and forth between the GPP and the GPU, instead of in the actual processing. A possible solution to this is an approach known as zero-copy operations, which consists of a unified address space for the GPP and the GPU that facilitates the passing of pointers between them, thus reducing the memory bandwidth requirements.
Similar benefits can be had by offloading correlation to reconfigurable hardware such as FPGAs. The correlation duties can be offloaded to an FPGA and the loop-closure and navigation engine can remain in the GPP. The FPGA is particularly well suited to the GNSS correlation tasks and can implement dedicated low-resolution (such as 1-4 bit) multiply-and-accumulate blocks, where the equivalent 8-, 16- or 32-bit operations on a GPP would be excessive or inefficient. Early approaches involved an FPGA connected as a peripheral device via Ethernet, Peripheral Component Interconnect Express (PCIe) or a similar bus. However, similar to the GPU, the data transfer quickly becomes a bottleneck. This challenge is addressed by integrating the GPP-FPGA packages. An early example of this approach was the Intel Atom E6x5C package hosting an Altera FPGA. More recent examples are Xilinx’s Zynq 7000 family integrating ARM and FPGA processors in a single encapsulation. These SoCs allow the direct injection of signal samples from the RF front end into the FPGA, greatly reducing the amount of information to be interchanged with the GPP. This approach provides flexibility with regard to how tracking and correlation resources are allocated, allowing configurable architectures according to the targeted signals of interest and application at hand, and enabling the execution of full-featured software-defined receivers in small form factor devices.
THE CLOUD
The ability to manage resources as logical entities instead of as physical, hardwired units dedicated to a given application has materialized in business models such as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructures as a Service (IaaS). A network of software-defined GNSS receivers executed in the cloud, appears to be the next natural step in this technology trend, in which the GNSS receiver is no longer a physical device but a virtualized function provided as a service (see FIGURE 4).
FIGURE 4. Illustration of the cloud-based GNSS signal-processing paradigm. (Courtesy of SPCOMNAV, Universitat Autònoma de Barcelona)
A virtualized software application is a program that can be executed regardless of the underlying computer platform. This can be achieved by packaging the application and all its software requirements (the operating system, supporting libraries and programs) in a single, self-contained software entity, which can be then run on any platform. An instance of a software-defined GNSS receiver executed in a virtual environment can then be called a virtualized GNSS receiver.
Early virtualization was in the form of full or machine virtualization (virtual machine or VM), which is a software application that emulates the hardware environment and functionality of a physical computer. With VMs, a software component called a hypervisor interfaces between the VM environment and the underlying hardware (CPU), providing the necessary layer of abstraction. A VM can run a full operating system, so conventional software applications (such as a software-defined GNSS receiver) can run within a VM without any required change.
Recently, the use of operating system virtualization or software containers has become more popular as they are often faster and more lightweight than VMs. Instead of a hypervisor, software containers use a daemon that supplements the host kernel, and can therefore be more efficient in making use of the underlying hardware. Examples of these software containers are Docker and Ubuntu Snaps. An example of an open-source software-defined GNSS receiver packaged as a Docker container is available.
Virtualized GNSS receivers bring important benefits in two fields: business-wise, as a technology enabler for new GNSS-based services; and also the use of GNSS SDRs as scientific tools, to ensure reproducibility.
As a service enabler, virtualized GNSS receivers allow for automatic and elastic creation, execution and destruction of application instances as required, and intelligent spread of the running instances across computing resources, regardless of processor architecture, host operating system or physical location. Several solutions are reported in the technical literature, many based on the GNSS snapshot-receiver, in which a short batch of data is sent to the software for position, velocity and time computation. Notable examples of such an approach are Microsoft’s energy-efficient GPS sensing with cloud offloading and the system running on Amazon Web Services. These approaches allow extremely low power consumption to the user equipment, at the expense of limited accuracy (ranging from 10 to 100 meters of error) and high latency. Commercially, Trimble offers Catalyst, a subscription-based GNSS receiver cloud-based service for which the user is charged according to the provided accuracy level, although the exact details are not yet public.
Virtualization technologies also offer a convenient solution for security-related applications (such as GPS M-code and Galileo PRS), since the encryption module remains on the service provider’s premises, and there is no need for a security module in the receiver equipment. This approach may enable the widespread use of restricted/authorized signals by the civilian population.
Finally, virtualization also offers important benefits for science. The flexibility of SDR receivers makes them an ideal tool for scientific experiments, since an implementation released under an open source license would allow a scientist to share a complete description of the processing from raw signal samples to the final research results.
STANDARDIZATION EFFORTS
GNSS signals are generally introduced to the front end through a standard interface, perhaps an SMA, MCX, or U.FL RF connector, and the digitized signals depart through another standard interface, perhaps USB, PCIe, or RJ45. However for a GNSS SDR, this is where the standardization ends. As discussed above, it is clear that there is a wide range of possibilities when capturing and digitizing a GNSS spectrum. Before processing this stream of digitized samples, details such as sample rate, center frequency, sample resolution and format/packing, and a variety of other parameters must be established. This is particularly important in a variety of scenarios such as when sharing/post-processing archived datasets in scientific applications, when offloading computational burden to a cloud-computer, or when interfacing different data-capture devices with different receivers. Ad-hoc methods of digitized data formats do not encourage interoperability and instead cultivate the potential for technology segmentation.
To address this challenge, The Institute of Navigation has lead an effort to develop a specification for standardized metadata, which would accurately and unambiguously describe the digitized data. Adoption of this metadata standard both by the data collection hardware and the software-defined radio receiver can promote interoperability, and can reduce the potential for error. Similarly, an SDR processor’s utility is extended when it is capable of supporting many file formats from multiple sources seamlessly. For more detail on the initiative, readers are encouraged to visit sdr.ion.org.
NEW FRONTIERS: GNSS SDRS IN SPACE
In space, GNSS receivers need to operate in scenarios that are quite different from those of ground-based receivers: higher (albeit predictable) dynamics conditions, low signal-to-noise-density ratios and poor positioning geometry. It is then an excellent scenario for SDRs, since it requires non-standard features from the receiver.
However, space is a harsh environment for semiconductor devices. Charged particles and gamma rays create ionization, which can alter device parameters. In addition to permanently damaging complementary metal-oxide semiconductor (CMOS) ICs, radiation may cause single-event effects, which are caused by ionizing radiation strikes that discharge the charge in storage elements, such as configuration memory cells, user memory and registers. When those effects happen, the system is usually recoverable with a power reset or a memory rewrite, but they also may destroy the device.
Until recently, radiation-hardened solutions were limited to application-specific integrated circuits or ASICs and one-time-programmable solutions. However, recently there has been an increase in the availability of space-grade FPGAs and memory devices. As examples, we can mention Xilinx’s Virtex-5QV, Microsemi’s RTG4 and Atmel’s ATF80 FPGA processors, and commercial SDR platforms such as GOMspace’s GOMX-3. Those devices allow the implementation of space-qualified GNSS receivers fully defined by software.
SDR receivers offer both reprogrammability (or upgradeability) and self-healing (or auto-remediation) capabilities. Examples could be the possibility to upload algorithms yet-to-be-invented at the receiver’s launch time, or the ability to recover from a single-event effect by remotely rewriting damaged functionalities, reducing the need of onboard redundancy.
THE ECONOMICS OF SDRS
Flexibility has a cost—and more flexibility costs more. This is why an FPGA implementation of a complex system can never compete with the unit cost of a fixed function ASIC. An example of a virtuous overlap might be seen in the Maxim 2120 and 2112 line of DVB-S2 TV receiver ICs, which have been successfully co-opted for GNSS SDR front ends due to their features (configurable mixers, gains, filters, operating power range and so on), which happen to be a good-enough match for the GNSS domain. On initial inspection, this allows for flexibility between the two application spaces and provides an ideal platform for SDRs supporting both TV decoding or GNSS on the same hardware radio module, but soon problems appear. The MAX21xx series are designed for TV applications, and TV applications tend to use 75-ohm input impedances while GNSS has standardized on 50 ohms. Certainly, one could add a software-defined impedance-selector block to the design, but we are now spending real hardware resources to accommodate SDR options. Adding an application that requires reception and transmission such as Wi-Fi, adds an entire signal chain to the design, as well as a large increase in the required dynamic range of the system. Adding an application that exploits MIMO, multiplies the hardware resources needed.
The flexibility of SDR makes it an indispensable research, development, validation and hobbyist tool, but system design is about target selection and trade-offs. To quote one of the most successful engineers of the current era and Eckert-Mauchly Award winner Dr. Robert P. Colwell: “Pick your [technical] targets judiciously. … Pick your vision and then chase it. You can’t pick everything as your vision, that’s a recipe for mediocrity. If you can’t pick your target you’re not going to hit any of them.” For SDR-based systems, this would seem to mean that we should focus on applications where the flexibility afforded offsets the inevitable platform cost push, or where it allows targets of opportunity that require a subset of the capabilities of the platform already being used.
At the same time, our earlier definition of an SDR as “a reconfigurable radio system whose characteristics are partially or fully defined via software or firmware” means that SDRs are already everywhere around us on some level. Cellular phones provide an example of devices that connect a large number of hardware radios to a dizzying array of applications that process, consume, modify and sometimes retransmit the received data, while consumer devices such as wireless routers can often add support for protocol changes or tweaks via firmware. While the economics might prevent radio systems from being universal on all dimensions, there are very few radio devices now sold that don’t expose at least a few parameters via software.
CONCLUSION
It seems that we are at an interesting epoch in the evolution of the software-defined GNSS receiver. The GNSS community has begun to springboard off developments and advances in RF equipment and is enjoying both an increase in functionality and a reduction in cost.
Simultaneously, the software-defined GNSS receiver architecture has morphed in multiple directions, enjoying virtually unlimited processing power of cloud computing, or availing itself of fully integrated RF and host-processor modules. As the use cases and host environments for GNSS receivers continue to diversify and the need for flexibility in the receiver continues to increase, it may be that the software-defined GNSS receiver emerges as a contender for the ASIC receiver for certain specialized use cases. Furthermore, as navigation is increasingly provided by an internet-connected device, the software-defined radio may even carve out its own niche, to become the go-to solution.
ACKNOWLEDGMENTS
The authors thank Sanjeev Gunawardena at the Air Force Institute of Technology and José López-Salcedo of Universitat Autònoma de Barcelona for their discussions and correspondence and for providing valuable insight and suggestions.
JAMES T. CURRAN received a Ph.D. in electrical engineering in 2010 from the Department of Electrical Engineering, University College Cork, Ireland. He is a radio-navigation engineer at the European Space Agency in the Netherlands.
CARLES FERNÁNDEZ-PRADES received an M.Sc. and a Ph.D. in electrical engineering from the Universitat Politecnica de Catalunya, Barcelona, Spain, in 2001 and 2006, respectively. In 2006, he joined Centre Tecnològic Telecomunicacions Catalunya, Barcelona, where he holds a position as senior researcher and serves as head of the Communications Systems Division.
AIDEN MORRISON received his Ph.D. in 2010 from the University of Calgary, where he worked on ionospheric phase scintillation characterization using multi-frequency civil GNSS signals. He works as a research scientist at SINTEF Digital in Trondheim, Norway.
MICHELE BAVARO received his master’s degree in computer science from the University of Pisa, Italy, in 2003. After working for several organizations including his own consulting firm, he was appointed as a technical officer at the Joint Research Centre of the European Commission in Brussels. He now works at Swift Navigation in San Francisco, California.
Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.
A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, published by Birkhäuser Engineering, Springer-Verlag GmbH, Heidelberg, 2007.
“Snapshot Positioning for Unaided GPS Software Receivers” by Y. Qian, X. Cui, M. Lu and Z. Feng in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 16–19, 2008, pp. 2343-2350.
• Cloud GNSS Signal Processing
“A Cloud Optical Access Network for Virtualized GNSS Receivers” by C. Fernández-Prades, C. Pomar, J. Arribas, J.M. Fàbrega, J. Vilà-Valls, M. Svaluto Moreolo, R. Casellas, R. Martínez, M. Navarro, F.J. Vílchez, R. Muñoz, R. Vilalta, L. Nadal and A. Mayoral in Proceedings of ION GNSS+ 2017, the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 25–29, 2017, pp. 3796–3815.
“Computational Performance of a Cloud GNSS Receiver Using Multi-thread Parallelization” by V. Lucas-Sabola, G. Seco-Granados, J.A. López-Salcedo, J.A. García-Molina, and M. Crisci in Proceedings of Navitec 2016, the 8th Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, Dec. 14–16, 2016, doi: 10.1109/NAVITEC.2016.7849357.
“CO-GPS: Energy Efficient GPS Sensing with Cloud Offloading” by J. Liu, B. Priyantha, T. Hart, Y. Jin, W. Lee, V. Raghunathan, H.S. Ramos and Q. Wang in IEEE Transactions on Mobile Computing, Vol. 15, No. 6, June 2016, pp. 1348–1361, doi: 10.1109/TMC.2015.2446461.
• High-Performance RF Sampling
“A 13b 4GS/s Digitally Assisted Dynamic 3-stage Asynchronous Pipelined-SAR ADC” by B. Vaz, A. Lynam and B. Verbruggen in Proceedings of 2017 ISSCC, the IEEE International Solid-State Circuits Conference, San Francisco, California, Feb. 5–9, 2017, pp. 276-277, doi: 10.1109/ISSCC.2017.7870368.
u‑blox is offering the automotive-grade MAX‑M8Q‑01A GNSS module, which measures 9.7 x 10.1 x 2.5 millimeters and has an operating temperature range from –40 degrees Celsius to 105 degrees Celsius.
The MAX‑M8Q is the company’s third automotive-grade GNSS module to date, alongside the NEO‑M8Q‑01A and NEO‑M8L‑03A modules.
MAX‑M8Q‑01A is designed to meet the stringent requirements of the automotive market, providing superior positioning accuracy even in challenging environments such as urban canyons. Its extended temperature range ensures reliable performance even in harsh environments, e.g. when mounted in a car‑roof antenna.
Produced in adherence to the u‑blox 0 ppm program, which aims to bring down product failures rates to zero and consistently achieve high production quality, the module is delivered with the automotive industry’s standard PPAP documentation to ensure compliance with customer requirements.
The module offers product developers a reduction of design and qualification time and effort, shortening time‑to‑market and considerably reducing risks for new product development.
“We developed this automotive grade GNSS module in the small MAX form factor in response to customer requests for a GNSS receiver that operates reliably in an extended temperature range,” said Franck Berny, senior principal, automotive market development, u-blox. “We are confident that the module’s high quality, robust and secure performance, and small form factor will appeal to the automotive industry at large.”
A security center for the European Union’s Galileo satellite system will be moved from the United Kingdom to Spain as a result of Brexit, according to numerous press reports.
A committee of representatives of member states voted by a large majority on Jan. 18 to approve the European Commission’s recommendation of Madrid as the Galileo Security Monitoring Centre’s (GSMC’s) new home.
The center, which is not yet fully operational, has only one full-time member of staff in Swanwick, England, but when it is up and running in Madrid, staffing is expected to grow to as many as 30.
The center controls access to the satellite system and provides around-the-clock monitoring when the main security center near Paris is offline.
The European Commission’s decision to move the center to Spain will bring Spain “strategic advantages, industrial development of high technological value, and the consolidation of national knowledge and technology in the area of security,” the Spanish ministry of public works said.
Spain was selected from six countries, according to Spanish media. It offers the facilities of the National Institute of Aerospace Technology (INTA), which belong to the defense ministry and are located in Madrid.
The GSMC is operated by the European GNSS Agency (GSA) in charge of supervising and acting on cases such as security threats and alerts.
Spain has another of the fundamental centers of the program, the Loyola de Palacio GNSS Service Center, also located in Madrid.
The center is one of a number of EU institutions leaving the UK as a result of the 2016 referendum vote, also including the European Banking Agency, which is relocating to Paris, and the European Medicines Agency, which is going to Amsterdam.
The Republic of Korea Agency for Defense Development (ADD) has selected SimActive’s Correlator3D software. The agency’s use of the software will include processing UAV and satellite imagery.
The transaction was facilitated through a SimActive partner in the region, GeoFocus Inc.
“The software was originally developed for military clients, which is reflected in the processing speed and rigorous mapping standards the technology adheres to,” said Philippe Simard, president of SimActive. “We are proud to welcome ADD as they join governments worldwide using Correlator3D.”
Hexagon Geospatial will provide students and faculty at each of USGIF’s 14 accredited programs with three-year licenses for its desktop and cloud-based Smart M.App software.
The software is designed to benefit and assist students, professors, and scientists in building geospatial cloud applications.
Smart M.apps are interactive map applications that combine content, analytics, workflow, and presentation to solve a specific business problem.
“As a company with roots in universities across the world, Hexagon Geospatial has always valued students and academia as an investment in the future,” said Jason Sims, Hexagon Geospatial’s chief channel and marketing officer. “This is why we are so happy to announce our partnership with USGIF, providing access to our software and platforms. We look forward to seeing the way instructors, researchers, and students influence how location information will be used to innovate and shape smart change.”
USGIF’s Collegiate Accreditation Program prepares students with the necessary knowledge and skills upon entering the professional geospatial intelligence (GEOINT) workforce. USGIF-accredited GEOINT programs include Fayetteville State University, George Mason University, James Madison University, Universidade Nova de Lisboa, Northeastern University, Pennsylvania State University, the University of Texas at Dallas, the University of Utah, the U.S. Air Force Academy, the University of Missouri, the University of Redlands, the University of South Carolina, the University of Southern California, and the U.S. Military Academy.
“We’re excited about this partnership to collaboratively issue license grants to faculty and students teaching at and attending USGIF accredited institutions,” said USGIF Director of Academic Programs Dr. Camelia Kantor. “USGIF’s accredited programs have a track record consistent with excellence in preparing students for work in the GEOINT profession. Such partnerships bring academia and industry together to ensure the preservation of standards, to encourage innovation, and to enable faculty and students to teach, learn, and conduct research using software from industry.”
For those who want high accuracy, but don’t need it full time, high-productivity dedicated professional solutions may not be cost-justified. In these cases, a “positioning as a service” subscription could offer a viable use model.
Achieving precision positioning with just a standard mobile device, a correction stream using the mobile device’s data connection and a high-accuracy positioning application produces a very low barrier to achieving high accuracy.
ByStuart Riley, Herbert Landau, Victor Gomez, Nataliya Mishukova, Will Lentz and Adam Clare, Trimble Inc.
We expect that for professional applications that need precision positions, a dedicated system that employs a custom GNSS chipset and purpose-built applications will continue to be the right solution. However, it becomes clear that the ubiquity of consumer mobile devices, with increasing computing power, ruggedness and an expanding feature set, presents fertile ground for new development of improved positioning systems that don’t have strict professional requirements.
A range of new use models and applications will be enabled by consumer mobile phones with technology that improves positioning performance. The goal of the work presented here is to assess what level of performance can be achieved by using proprietary PVT (position, velocity, time) engines utilizing GNSS measurements from the Android GNSS measurement application programming interface (API).
We first review GNSS measurement and positioning performance from a subset of the current Android phones/tablets currently on the market. Then we show the position performance achievable using precision engine with measurements from a dual-frequency GNSS chipset targeted for the cellular handset market. This class of device is expected to be integrated into consumer cellular devices on the market within the next 1 to 2 years.
Performance of Current Phones
We tested various devices including the Nexus 9 (which provides phase data) and various other Android devices that implement the new API. Most devices tested do not support phase data; of the few devices tested that do provide phase data, all except the Nexus 9 implement GNSS power duty cycling. This is a mode where the GNSS chipset is only active for a fraction of each second to reduce power consumption. This results in cycle slips each epoch, which makes carrier-phase processing for real-time kinematic (RTK) unusable.
During the testing a wide range of performance across devices was observed. Figure 1 shows the C/NO for a high-elevation GPS satellite collected at the same time from two different Android models that implement the GNSS measurement API. The units were located in a clear environment less than a meter apart. Deep fades are present, most likely caused by deconstructive multipath.
Figure 1. Comparison of the C/NO from two different Android devices.
However, the devices show significantly different tracking performance: device B reports over 10 dB lower C/NO for much of the test and eventually stops reporting measurements. During our analysis, around six different Android devices have been tested; it isn’t clear whether the devices tested are typical over a broader population of device types.
Before attempting to position with observables from Android devices the measurement quality was analyzed. As only a subset of current devices that support the API provide phase information we wanted to evaluate both a phase-based RTK engine and a pseudorange/Doppler based code engine to determine what is possible from each class of device.
One of the devices tested was a Samsung S7 device. It provides pseudorange, Doppler and phase via the GNSS measurement API. However, the phone implements power duty cycling so after a short period of operation the duty cycling mode was enabled which resulted in a cycle slip on the phase every epoch.
To derive an improved position from this class of device pseudorange and Doppler can be fed into a code-phase positioning engine. Fortunately, the Doppler provided by the device is of reasonable quality as can be seen from Figure 2.
Figure 2. Android GNSS observables: Doppler versus time-differenced pseudorange.
In this simple analysis measurements from a single high elevation satellite were analyzed. The Doppler is plotted along with the differenced pseudorange converted into L1 cycles. It can be seen that as expected the Doppler has much lower noise and so can be used in a pseudorange smoother.
A simple way to view the pseudorange noise is to subtract the carrier phase from the pseudorange. If there are no cycle slips this should show ionospheric divergence with the noise dominated by the pseudorange noise. The absolute level is arbitrary as it includes integer carrier cycles. Figure 3 shows an example from an Android device.
The data was captured on a building roof in an open environment. There’s a slight downward trend due to the ionospheric divergence between code and carrier, but the metric is dominated by the pseudorange noise. For this example from a high elevation GPS satellite the standard deviation is 6.5 meters. For comparison, a precision receiver connected to a precision GNSS antenna providing unsmoothed pseudorange in this environment would have a standard deviation of a few decimeters.
Another way to assess the measurement performance is to form double difference residuals. Data was logged from pairs of identical devices mounted with a common orientation. An RTK system was used to measure the same point on each device. The camera lens location above the screen was used as the reference point.
An accurate vector between the two references points was computed and used as truth in a double-difference residual analysis. Even though we do not know the precise location of the phase center of the antenna, because the difference was performed between two devices that are the same model and have the same orientation the error in the phase center location is common and will cancel. Various pairs of devices were tested by being mounted on a wooden board on a tripod at approximately waist height. The test configuration is shown in Figure 4.
Figure 4. Android device test configuration.
Figure 5 provides the double difference GPS L1 C/A pseudorange residuals between two Android devices. We see errors beyond 100 meters and a standard deviation across all data of 14.4 meters. A precision system (RTK or RTX/PPP) would use a standard survey quality base or network of bases and not an Android device for the correction data.
Figure 5. Short baseline double-difference pseudorange, Android devices.
Consequently in a typical operating mode where a precision data stream provides corrections, the contribution in a double difference from the pseudorange on the Android devices would be roughly half the Android-to-Android residual seen in this test or approximately 7.2 meters for this example.
For comparison, the same metric was generated between two precision GNSS units connected to antennas on the same roof. While the data was not from the same time period, we observe very consistent performance over time.
Figure 6 shows the same pseudorange double difference across a short baseline over 24 hours. When comparing Figures 5 and 6, note the difference in the scale on the pseudorange residual axis. The standard deviation from a pair of precision devices is 53 centimeters (cm) or 27 times lower noise than an example pair of Android devices.
Figure 6. Short baseline double-difference pseudorange, precision devices.
All phones that provide GNSS measurements via the Android API publish the phase data in the accumulated delta range field. An accumulated delta range is not necessarily a full phase measurement; it can have an arbitrary starting phase.
For example, in a precision GNSS receiver, if the receiver locks to a satellite and some time later locks a second channel to the same satellite, the phase measurement from the two channels may have a different integer cycle component, but the subcycle component would be the same except for millimetric tracking noise.
If the two channels are providing accumulated delta range the initial phase offset may differ by up to one cycle. From the population of Android devices that publish phase that we have tested we have not observed any devices that deliver true full phase.
They all deliver an accumulated delta range with an arbitrary phase offset. This limits a phase engine to float processing and ambiguity fixing is not possible. The Android phase data collected from the previously described experiment was processed to provide the double difference carrier residuals. This is shown in Figure 7.
Figure 7. Short baseline double-difference phase residuals, Android devices.
The y-axis is in millicycles (1,000 millicycles = 1 cycle or approximately 19 cm for L1 GPS). Jumps are seen as the reference satellite changes or when the measurements have cycle slips. In this case the standard deviation is 342 millicycles. A double difference residual on a precision receiver in a similar environment with a high-quality antenna on a short baseline is an order of magnitude lower than this.
Another useful metric to review are the number of reported cycle slips. Figures 8 and 9 show a comparison of the cycle slips reported on GPS L1 C/A from an Android device compared to data logged on a precision receiver over the same time span. The receiver tends to only cycle slip at low elevation; the device had a zero-degree mask. The Android GNSS device cycle slips at higher elevations, probably a result of deep multipath fades due to the poorer antenna.
In an ION GNSS+ 2017 paper, we showed the achievable position performance using an RTK engine that had been previously customized to operate with measurements from consumer GNSS chipsets. It operated in a float mode due to the sub-cycle issue found in phase data from Android devices.
We also demonstrated the performance from a precision code-based PVT engine that had changes to the a priori measurement error estimate, a modified pseudorange/Doppler Hatch filter and used SBAS data to correct the position. As very few current Android devices deliver phase information the two engines were used to analyze what is possible today with the pseudorange and may be available in the future as phase is more universally available.
Data was processed from a Nexus 9 tablet, the only known Android device that has GNSS power duty cycling disabled. The unit was unmodified and so the Android tablet’s integrated GNSS antennas were used. The 2D performance is given in Table 1.
Table 1. 2D performance from Nexus 9 Android tablet.
Only GPS L1 and GLONASS L1 measurements were used and the RTK float solution delivered similar performance to the pseudorange solution. This is due to a combination of issues, very high pseudorange noise, and a significant number of cycle slips (see Figures 5 and 8). Only single frequency data was available, and while the engines used had been tuned for consumer data, they were not specifically designed for this class of data.
Next-Generation Phones
Within the next couple of years improved chipsets are expected to be available to consumers that will result in improvements in achievable positioning performance. In May 2017, Broadcom provided us with a development kit for its next generation L1/L5 multi-system BCM47755 GNSS chipset. This allowed us to assess what may be possible when improved GNSS chipsets are integrated in the next generation of cellular devices.
Figure 10. Broadcom BCM47755 development system.
The development environment included the GNSS chipset with an external antenna port so both a cell-phone equivalent antenna and a precision antenna could be compared. This allowed us to evaluate the impact of the antenna performance on the GNSS observables and positioning results. The Broadcom GNSS development system communicates via USB to a Samsung S7 phone and publishes data via the Android GNSS measurement API so the equivalent data flow of an integrated cellular device is maintained (see Figure 10).
In our ION paper, we showed the typical phase double-difference residuals observed from current Android devices. The Broadcom BCM47755 originally provided similar performance, although it also supports GPS L5 and Galileo E5A. In November 2017, Broadcom provided a firmware update that resolved the sub-cycle phase issues. With the updated Broadcom software, the double difference carrier residuals for GPS L1 on a zero baseline when differencing a precision receiver to a Broadcom BCM47755 are shown in Figure 11.
Figure 11. Precision GNSS to Broadcom BCM47755 zero baseline double difference carrier-phase residuals.
The standard deviation is 45 millicycles which is approximately 8.6 millimeters (mm). This is substantially better than earlier implementations of the Android GNSS interface (see Figure 7) and sufficient to perform RTK ambiguity resolution.
The rest of the results in this article were obtained with the improved firmware along with a new precision position engine. This engine was designed from inception to support GNSS measurements with differing quality and so can more optimally process the Android GNSS data. The effect of the improvements to the Broadcom firmware and the change in the processing engine can be seen if the results in our ION paper are compared to the data in this section.
To attempt to model what may be possible with a phone based on a next-generation chipset, a cell-phone equivalent antenna provided by Broadcom was used in some of the tests with the development system, as shown in Figure 12. This device has separate feeds for L1 and L5.
Figure 12. Cellular equivalent antenna.
Datasets were collected with the multi-frequency GNSS BCM47755 device. The data was captured in the Android GNSS measurement API format and converted to proprietary format files for further processing. All data was collected in Sunnyvale, California.
Measurements from GPS L1/L5, Galileo L1/E5A, GLONASS L1 and BeiDou B1 were logged and analyzed. The Precise Positioning Engine (PPE) allows performing carrier-phase RTX and RTK and a pseudorange-based solution using the RTX corrections. Tests were performed by using a precision antenna and a cell-phone equivalent GNSS antenna.
With Precision GNSS Antenna
These datasets were collected on a zero baseline with a precision receiver to allow a direct comparison of results with a professional receiver. The first test was on Nov. 22, 2017, where the Broadcom GNSS chip and the receiver were connected to the same professional antenna.
As seen in Figure 13, both GNSS receivers provide centimeter-level accuracies after some convergence time. With the current satellite constellations, only a third of the GPS satellites have L5 and only about half of the E5-capable Galileo constellation is in space. During this 3.5-hour test, the number of dual-frequency measurements processed by the engine that used the Broadcom chipset — data that does not support L2 — ranged between 6 and 10 satellites (Figure 14).
Figure 13. RTK performance for a 3.5-hour dataset sampled on Nov 22. Broadcom chip at left and precision chip at right. A short baseline was used — precision antenna.Figure 14. Number of GPS L1/L5 plus Galileo E1/E5A dual-frequency measurements used by the position solution based on the Broadcom chipset — precision antenna.
Convergence times were measured with post-processing tools by splitting the datasets into individual time spans. Figure 15 shows that the consumer GNSS chipset is able to get fixed ambiguity solutions but it takes considerably more time (266 seconds versus 4 seconds) for the 95% of initializations. However, the system is fixing ambiguities and provides centimeter level positioning.
The same datasets were also processed with RTX-Fast in California. Thus the base station data was replaced by a global/regional correction stream received from an internet-based data source (Figure 16).
Figure 15. RTK initialization performance, dataset sampled on Nov 22. Broadcom chip at left and precision receiver at right — precision antenna.Figure 16. RTX performance for a 3.5 hour dataset sampled on Nov. 22 (Broadcom chip at left and Trimble chip at right) — precision antenna.
Horizontal accuracy for Broadcom reach 10 cm while the precision receiver reaches better than 3 cm. The degradation is in part due to the difference in quality of the carrier phase and the different number of dual frequency satellites processed. Precision devices provide measurements on E1/L1, L2 and L5/E5 providing at least dual frequency data from GPS, GLONASS, Galileo, BeiDou and QZSS.
The Broadcom chipset tested provided dual frequency GPS and Galileo along with single-frequency GLONASS and BeiDou; however, due to limited BeiDou constellation visible in California, data from this constellation was not used.
Convergence was also analyzed and is shown in Figure 17. From the data, we generated 24 convergence runs by taking one hour, progressively shifting the start time by 5 minutes and running the data with different start times through the PPE engine. This produced 24 runs, which were translated into 68% and 95% convergence statics shown.
Figure 17. RTX convergence performance for a 3.5-hour dataset sampled on Nov. 22. Broadcom chip at left and precision chip at right — precision antenna.Figure 18. Code RTX performance for 3.5-hour dataset sampled Nov. 22 and corresponding RTK and RTX phase solutions — precision antenna.
The RTX-Fast solution for Broadcom reaches 30 cm horizontal error in 68% of the cases in approximately 12 minutes. The RTX-Fast convergence using precision GNSS data is near instantaneous as can be seen in the right of Figures 16 and 17, reaching centimeter accuracy.
The code position solution using the RTX correction stream provides sub-meter positioning (Figure 18).
As a summary, the cumulative distribution function plots (Figure 19) show the performance differences for this static environment, on Nov. 22.
Figure 19. CDF plots for the different PPE position solutions — precision antenna.
Cell-Phone GNSS Antenna Results
Similar tests were performed using an external cell-phone GNSS antenna, which is close to the antenna used in a typical smartphone. RTK performance shows centimeter-level accuracies and reasonable convergence times, which are slightly worse than the results with the professional antenna (Figures 20–24).
Figure 20. RTK positioning and initialization performance for the Broadcom chip and the cell antenna sampled on Nov 20 — cell-phone GNSS antenna.Figure 21. RTX-Fast positioning and convergence performance for the Broadcom chip and the cell antenna sampled on Nov. 20 — cell-phone GNSS antenna.
In general as expected we achieve worse performance when connected to the GNSS cell-phone antenna for all the different positioning modes. For the cell antenna we also generated single-frequency RTK and single-frequency RTX-Fast position solutions and compare it with a code positioning solution.
Positioning Engine in Android
Figure 22. Number of GPS L1/L5 plus Galileo E1/E5A dual-frequency measurements used by the position solution based on the Broadcom chipset — cell-phone GNSS antenna.
The results presented in this article captured GNSS data using the Android API and then post-processed the data using PC versions of the position engines. A significant amount of data has been captured and analyzed using this method.
For the purpose of real-world demonstration the PPE has been implemented in an Android app to be used in cell phone devices. This PPE is able to provide RTK, RTX and code based positioning technology in one single PPE library.
The app has been tested running on a Samsung S7 connected to Broadcom’s new chipset development kit as well as a Nexus 9 tablet that uses an older generation GNSS chipset.
Figure 23. Code RTX performance, the dataset sampled Nov. 20 and corresponding RTK and RTX phase solutions — cell-phone GNSS antenna.
Future work will refine this solution as well as evaluate how well the system works when mobile. The data collected in this article operated in an environment with a clear view of the sky. We plan to characterize what happens when the platform moves with both pedestrian and automotive dynamics, as well as the effects of body masking and challenges with changes to the GNSS antenna reception pattern when the phone is held.
Summary
While this article has highlighted that sub-meter and centimeter accuracy have been achieved in a laboratory environment, there are many challenges to be addressed before centimeter accuracy in a phone can be achieved with performance suitable for users in real-world environments.
Figure 24. CDF plots for the different PPE position solutions for cell antenna dataset.
The challenges include very high multipath, significant differences in the tracking performance between different devices, and high rates of cycle slips. As very few Android-based devices provide continuous phase, a pseudorange/Doppler-based engine has been modified to accept Android data.
Based on the testing with existing devices it is possible to achieve position solutions of 1–2-meter accuracy in ideal static scenarios. This is a significant improvement in accuracy for Android based devices.
Figure 25. PPE engine on a Samsung S7 with a Broadcom BCM4775 evaluation kit.
However, as performance differences were observed between different mobile devices significantly more data needs to be collected over a larger set of devices to review the repeatability of these preliminary results from existing Android devices.
The Broadcom BCM47755 development kit for a dual-frequency GNSS chipset intended for future phones has allowed us to review the potential position performance that may be achievable in a handset in a few years.
By connecting this next-generation GNSS chipset to a GNSS antenna typical of a cellular device and comparing the performance from a precision GNSS antenna, we’ve shown for the first time that it is possible to produce precision positions from a static cellular class GNSS device in ideal conditions at the centimeter level with both an RTK solution and a PPP solution.
However, due to the significantly higher measurement noise and high multipath from the cellular device’s GNSS antenna, the convergence times to reach centimeter level remain a challenge; although using dual-frequency phase data from a cellular GNSS chipset with a PPE and RTX service, the position is very rapidly sub-meter.
Future work will focus on analyzing how the performance changes when operating in the normal user environment. The effects on the measurements of user motion, body masking and de-tuning of the antenna when the device is held need to be quantified. The Nexus 9 tablet used in this article does not have integrated cellular. The Broadcom development kit connects to the phone via a cable and is also not integrated into the handset.
We will be evaluating what may happen with a more integrated unit to make sure emissions from devices with integrated cellular very close to the GNSS antenna do not result in further degradation.
As the position performance is very sensitive to the quality of the antenna from both multipath and cycle slips due to low C/NO and deep fades, we’ll also evaluate how well the performance of the PCB-based GNSS antenna, which is part of the BCM47755 evaluation kit, matches current handsets.
Acknowledgment
This article further develops work first shown in an ION GNSS+ 2017 paper, “On the Path to Precision — Observations with Android GNSS Observables.”
Manufacturers
Trimble CenterPoint RTX is the satellite orbit and clock corrections service used here, enabling a PPP-like positioning with ambiguity fixing, providing better than 4 cm with typically less than 10 minutes’ convergence.
RTX-Fast functionality in Europe and parts of California uses regional atmospheric models to provide better than 4-cm horizontal in typically less than one minute. When precision and professional receivers and RTK engines are mentioned in this article, they are Trimble devices, the BD940 receiver in some cases.
A Trimble Zephyr 3 antenna was used in many tests shown here.
The global market for precision agriculture solutions is forecast to grow from €2.2 billion ($2.6 billion) in 2016 at a compound annual growth rate (CAGR) of 13.6 percent to reach about €4.2 billion ($5 billion) in 2021, according to a research report from the market analyst firm Berg Insight.
A set of technologies are applied in precision farming practices that are aimed at managing variations in the field to maximize yield, raise productivity and reduce consumption of agricultural inputs. While solutions such as auto-guidance and machine monitoring and control via onboard displays are mainstream technologies in the agricultural industry, telematics and variable rate technology (VRT) are still in the early stages of adoption.
Interoperability between hardware and software solutions remains a challenge, although standardization initiatives led by organizations such as Agricultural Industry Electronics Foundation and AgGateway are making progress.
Most major agricultural equipment manufacturers have initiatives related to precision agriculture, although strategies vary markedly. Leading vendors include agricultural equipment manufacturer Deere & Company, followed by the U.S.-based precision technology vendors Trimble, Topcon Positioning Systems, Raven Industries and Ag Leader Technology. Hexagon further holds a strong position in the positioning segment through its subsidiary NovAtel.
A group of companies have emerged as leaders in the nascent market for in-field sensor systems. These include Davis Instruments, Pessl Instruments with its METOS brand, Semios, Hortau, AquaSpy and CropX.
By Bradford Parkinson Vice-chair, U.S. PNT Advisory Board
In the coming months, the U.S. Federal Communications Commission (FCC) may allow high-powered, ground-based, communication transmitters to broadcast at a frequency near GPS L1. U.S. Department of Transportation (DOT) tests have shown that such transmitters effectively become jammers for many existing GPS receivers.
I believe that this possibility is the greatest current threat to the position, navigation and timing (PNT) community.
L1 is the primary band for GPS as well as for similar GNSS. For example, the international signal called L1C is to be centered at L1, albeit with wider spreading than the current L1 civil signal, C/A.
Why is this of critical importance? An economics study that only considered a small subset of benefits concluded that the U.S. alone realized $65 billion per year in direct economic value. A more complete recent study for the UK, extrapolated to the U.S., estimated the total impact of the loss of GPS to be over $3 billion per day for a five-day outage — a far greater rate. Virtually all GPS applications rely on the signals at L1. Thus, any threat to GPS is not simply an inconvenience, it would have great potential to do economic harm.
The PNT Advisory Board (PNTAB)has been trying to protect PNT, particularly GPS, and at the same time accommodate Ligado, a company that has requested repurposing of nearby spectrum. At our November meeting, we reviewed the Ligado proposal and framed a response that will be made public in due time. Meanwhile, these observations and conclusions are my own.
History
In 2011, LightSquared proposed that existing restrictions on its existing frequency authorization in the Mobile Satellite Service (MSS) band (a faint signal, satellite-to-ground) be waived so that the band is effectively repurposed to allow for high-power terrestrial transmissions.
The company has two space-to-ground authorizations in the 1525–1559 MHz band (1526–1536 MHz and 1545–1555 MHz) very close to the GPS primary frequency (L1 at 1575MHz). Initially it requested repurposing to ground transmission of 42 dBW (15.8 kW).
Faced with tests and analysis that showed this would be very destructive to GPS, it proposed to abandon the closer band and reduce power in the further band to 32 dBW, or 1580 Watts.
Ligado filings suggest a spacing of approximately ¼ mile between transmitters. A GPS receiver would find even these weaker signals 5 billion times the power of GPS at the maximum range of ¼ mile.
Most PNT users would be much closer.
International criterion
To ensure ranging accuracy, the international standard for interference to GPS is a 1-dB increase in noise levels. In conventional terms, this max allowable 1 dB is a 25.8% increase in background noise. The power of the weak GPS signal is only about 1% of the background radio noise. Sophisticated signal processing algorithms allow the signal to be reconstructed.
The result: the international 1-dB standard is equivalent to a 25% reduction in GPS radiated power.
Two additional points
The 1 dB is not simply to protect signal lock, it is to protect ranging accuracy. Most GPS receivers will stay locked for higher levels of interference but lose high precision. This is particularly a problem for high-precision receivers, which need relative timing to sub-nanosecond accuracies.
These measurements are equivalent to the time it takes light to travel ¼ inch. Protecting such accuracies is of paramount importance to PNT users and applications.
Allowing such maximum degradation from a single source is not the whole picture. There are many other potential sources of interference and attenuations of the GPS signal. For example, foliage may reduce the GPS signal.
A receiver must cope with all of these difficulties. Allowing a single cause, such as the Ligado repurposing, the 25.8% equivalent reduction might be considered quite generous, but it is the accepted International Standard.
Ligado has specifically rejected this criterion, largely because testing has shown that the Ligado repurposing would then be unacceptable for many PNT user classes.
To support its rejection of the International Standard, Ligado has repeatedly alleged that five of the major manufacturers are in complete agreement regarding its repurposing. This is a substantial distortion. The record was set straight by Brian Ramsay of MITRE at the November PNTAB meeting: “Four of the five parties that reached agreements with Ligado (except for Topcon Positioning) support the 1-dB Interference Protection Criterion (IPC) in comments filed in response to this Public Notice.”
Further support was highlighted by Captain Robyn Anderson: “In June 2017, the Air Force produced a white paper on the 1-dB IPC that explained the relationship between harmful interference (levels that affect GPS receiver performance) and the 1-dB IPC (keeps interference below a level that would cause harmful interference).”
Lightsquared’s motivation in 2011 was clear: a $10 billion windfall profit (estimated increased value of the spectrum on open-market auction). The FCC did not confirm Lightsquared’s modified request, and in 2012 the company went into bankruptcy.
Reorganizing as Ligado and emerging in December 2015, it continued to pursue repurposing of its spectrum, sponsoring tests by Roberson and Associates, and tests at National Institute of Standards and Technology (NIST)/National Advanced Spectrum and Communications Test Network (NASCTN) to establish test procedures.
Both groups of tests were carefully reviewed by our PNTAB who found serious flaws. In general, Ligado rejected the 1-dB criterion and did not accept the need to protect all classes of users, particularly high-precision receivers. In addition, it did not consider the new GPS L1 signals (L1C and L1M), nor did it check the impacts on the international GNSS. The PNTAB assembled a 14-point summary of deficiencies and requested updates and corrections for the flaws.
NASCTN’S response did not really address the points, or claimed that there were no funds to correct the problems. The PNTAB then developed a Six-Point Criteria for acceptable interference testing,summarized as:
Accept and strictly apply the 1-dB criterion.
Verify interference for all classes of receivers.
Test and verify for all operating modes.
Focus analysis on worst cases.
Include the new GNSS signals.
Include GNSS expertise and openly publish results.
Image: PNTAB
We believe it is a very reasonable set that aims to protect PNT users and our economic benefits. In its sponsored tests, and in representations to the FCC, Ligado has consistently overlooked a basic facet of radio ranging: it is ranging accuracy, not simply locking onto a signal, that is the fundamental objective for PNT.
Both Ligado test sets clearly failed on all six points.
DOT ABC tests
While the Ligado-sponsored tests were neither independent nor adequate, the Department of Transportation, led by Karen VanDyke, sponsored a very complete set of independent tests; these are the most credible estimates of harmful interference. The ABC results have been made public. The PNTAB’s six points were published after DOT testing had begun, but DOT expanded and modified their effort to satisfy the criteria. The DOT conclusions, based on modeling real-world antennas and propagation patterns, are shown in Table 1.
TABLE 1. DOT ABC test results. Maximum tolerable effective radiated power (EIRP) for classes of the most susceptible GPS receivers for modified Ligado proposal (P2) of 1.58 kilowatts. In red are the factors that Ligado P2 exceeds the maximum tolerable radiated power. (Chart: GPS World)
At 100 meters, all classes of receivers tested had results that would exceed the 1-dB threshold, even for the reduced power level (P2, 1580 Watts) that has been the most recent filing. The shaded square is particularly troublesome. It shows that, for the most susceptible high-precision receivers, the Ligado proposed power exceeds the 1-dB threshold by over 200,000. This result is particularly damning for the proposed repurposing, because it is this class that produced the highest payoff in the recent Department of Commerce Study — over $30 billion per year.
PNT operations at risk
These are examples of unintended and potentially hazardous consequences of repurposing.
UAVs. Unmanned aerial vehicles (drones) will fly very close to the dense array of transmitters that Ligado would deploy. They usually require GPS for flight control. Even more important, if we are to monitor them and keep them from collisions, GPS offers the only viable techniques with 3D accuracy and almost 100% availability.
Precision survey. This is routinely used in urban areas for building construction and is a major source of productivity gains. These survey receivers are all high precision and routinely make measurements to better than ¼ inch.
Helicopters. These are found in urban area at all altitudes. They are used for law enforcement, rescue and passenger transportation. GPS is mainly used for general navigation.
Public safety vehicles. Fire, police and ambulances use GPS for both navigation and dispatch tracking. In a city, they would drive in and out of susceptible high-interference zones.
The PNTAB believes the DOT results are representative, accurate and credible. The National Coordinating Office for PNT also sponsored an evaluation of all testing to date. A summary report is now in coordination, as a combined Department of Defense (DOD) and DOT effort.
The DoD, which uses GPS in the national airspace for routine flight, testing, training, guiding rocket launches, and for humanitarian rescue missions, has opposed repurposing. The Air Force reported, “Results from the DOD ABC Assessment support the conclusions drawn from Department of Transportation’s ABC Assessment.”
November PNTAB meeting
At our November meeting, the board invited Ligado to make a presentation on its repurposing proposal. The invitation said: “Specifically describe your implementation plan, with a corresponding test plan addressing the issues we have openly raised. We request you specifically focus on those regarding the potential for interfering with any GPS/GNSS services that operate in the protected space-to-Earth L-band (1559–1610 MHz). Included should be all modes of operation and the use of all current and future GNSS signals.”
Valerie Green, executive vice president and chief legal officer of Ligado Networks, represented Ligado. In the run-up to the meeting, the Six-Point Criteria had been sent to Ligado. Green did not address the six points at all.
She did offer to reduce initial power to “the safe power level in the 1526–1536 MHz channel ranges from 9 to 13 dBW EIRP nationwide,not just near airports.”
FIGURE 1. Potential impacts on high-performance receivers. Red: loss of lock of all satellites. Yellow: loss of lock of low-elevation satellites. Green: 1-dB degradation. (Chart: PNTAB)
The 13 dBW corresponds to initial power levels of 19.95 W. However, Ligado has made clear in its FCC filings that it ultimately still wants a full 32 dBW base-station transmit power level, consistent with typical 4G/LTE networks.
The initial reduced power sounds like a major move in the right direction, but further questioning revealed two major issues:
Tower Spacing. Green was very evasive on the spacing of transmitter towers. Clearly, at the reduced power level, greater density would be needed to carry the original data bandwidth. At about 1/100th the power, density would have to increase by a factor of 100, and the spacing would have to decrease to 1/10th for the same data output rate.
Green referred us to an earlier filing which specified 0.25 mile, but did not clearly state that this was the plan; she claimed the details were proprietary. If this fundamental parameter, spacing, is not specified, it is hard to see the basis for the FCC evaluation of any new proposal. If the transmitter spacing is reduced to less than 1/10th of a mile, the sources of potential harm would be multiplied in a very worrisome way.
Future power constraint.A public presentation does not ensure that Ligado will actually file and agree to abide by those power constraints indefinitely. Board members pressed Green on the permanence of the power constraint.
She suggested it would be tied to the RTCA Minimum Operational Performance Standard. Revising the MOPS takes many years, if not decades, both to formulate and to implement. Retrofitting the commercial aircraft fleet is very expensive and time-consuming.
Further, her statement focuses only on commercial aircraft, ignoring the high-precision classes as well as future signals.
A modified summary chart (Table 2) for the lower power, based on the DOT ABC test results, shows that even at the lower power, the threshold for high-precision receivers is exceeded by a factor of over 3,000 at 100 meters. In fact, only cell phones, which are relatively inaccurate, could operate at 100 meters without exceeding the threshold.
TABLE 2. Results of DOT ABC test with Ligado transmitters constrained to 19.95 Watts (13 dBW). This illustrates that the International Interference Limit is exceeded many times over at 100 meters for certain high-precision receivers, highlighted in red. (Table: GPS World)
With these expectations and uncertainties, the PNTAB did not find the new revision acceptable to the PNT community.
Three fundamental issues
Ligado has steadfastly not accepted the realities of non-interference.
1 dB. Acceptance of the 1-dB (25.8% noise increase) International Interference standard is fundamental to protecting GPS applications throughout the country.
All current and future uses. Users of great concern are emergency services, helicopter and general aviation, UAVs, and precision survey and machine control. For example, many of the underground utilities in the U.S. have been mapped with precision, GPS-based, geographic information receivers. This application requires sub-meter accuracy and operates in both rural and urban environments.
Ligado has tended to simply focus on certified aviation, claiming that protecting that class of user is enough. The PNT community rejects that view. All current and future PNT users must be protected.
Worst–case interference. The recent round of testing was largely in a laboratory. Extrapolating to the real world must examine the situations with greatest interference. For example:
Number of simultaneous interfering transmitters. A single transmitter situation is not typical; three or more are apt to be in range. The additive power must be considered.
Propagation models. Propagation models for communications differ from those for evaluating potential interference to a navigation signal. For assured communication, a typical model assumes transmitted signal fall-off a little faster than 1/(distance squared). Ligado would naturally prefer to use this model, which is far from worst-case for interference. The early round of tests in Las Vegas verified the communications model would vastly underestimate interference levels, by factors of 10 or more. A more realistic model must be used.
Degradation Radius. This is the size of the circle within which the International Standard is violated for receivers in a specified class. If the spacing of transmitters is 400 meters, and the degradation radius is 200 meters, virtually all receivers are in the degradation zone. Ligado suggested an appropriate degradation radius is 250 feet for aviation (approximately 100 meters). Thus, they claim the PNT community should tolerate violation of the standard when closer than 100 meters to their transmitters. At 400 meters spacing, 25% of the area would be in violation.
But the ABC test results reveal a much graver situation. They show that, for the current Ligado proposal (1580 watts), the degradation radius is over 14 kilometers for high-precision receivers. See Figure 2.
The 1-dB criterion is the correct, accepted and somewhat generous allocation of interference that can be accepted by the PNT community. We would hope that the FCC would continue to insist on this standard.
PNT users must, yet again, defend the spectrum vigorously. Most of us are scientific and technical people. We are not used to discussions that deliberately avoid the technical issue or deny scientific evidence. We reject arguments that violate the fundamental laws of physics.
The currently filed proposal, 1580 Watts at spacing of ¼ mile, is unacceptable. It will do grave harm to many important PNT applications
We must be very leery of the new proposal by Ligado of 9–13 dBW. It still would violate the 1-dB criterion at 100 meters for many PNT users.
Moreover, the company history has been to bait and switch; it has an authorization for MSS Ancillary Terrestrial Component (MSS ATC) stations to fill the gaps in satellite coverage with ground transmitters. These must operate in conjunction with the space-to-ground link that made them effectively self-limiting. However, in 2011, it almost succeeded in switching this to a ground-only system, which would have achieved a huge financial windfall.
Open-air verification
If the FCC continues to consider this proposal, there is one step that it should take before granting it. It should require Ligado to deploy an array of transmitters in its advocated configuration, and run real-world, open-sky testing to assess the harm that may result, particularly to high-precision accuracy.
Such testing was done when the issue was first raised in 2011 and conclusively demonstrated unacceptable interference. Nothing has really changed from the baseline that was tested and found unacceptable then.
The company should carry the full financial burden of such a verification, under PNT supervision. The government, having already spent millions of dollars to defend the spectrum, should not bear the cost of such retesting.
Without this confirmation, it is hard to conceive of putting GPS and PNT at significant risk to satisfy investors who want to flip a company, after gaining “rezoning” permission for their spectrum.
From 20,000 feet altitude
If we examine the situation without the technical details, we have this: Fundamentally Ligado wants to provide service using its allocated frequency band for an unlimited number of Internet-of-Things installations.
It is not proposing a small, fixed number of transmitting towers located in isolated regions, but rather an accelerating deployment of private networks, many of which will be close to commercial and essential infrastructure where GPS use is critical.
It seems unrealistic that Ligado can or will reliably guarantee that these widespread installations will be continually adjusted and monitored to avoid GPS interference.
I believe the concept of allowing the installation of transmitting towers that, by design, will interfere with normal GPS use at some distance away opens the door to tacit approval of short-range (or not-so-short-range) GPS jammers.
While I can commend the entrepreneurial spirit, the Ligado proposal seems very reckless indeed. The incremental value of an additional broadband transmitting system when there are at least five already in existence seems trivial compared to the potential damage done to the modern utility named GPS.
I sincerely hope the FCC can find a spectrum swap or deny outright the current Ligado application.
AeroVironment Inc., a maker of unmanned aircraft systems (UAS) for defense and commercial applications, has formed a joint venture to develop solar-powered high-altitude long-endurance (HALE) UAS for commercial operations.
This category of unmanned aerial systems (UAS) is also referred to as high-altitude pseudo-satellites, or HAPS.
The joint venture will fund the development program up to a net maximum value of $65,011,481.
The joint venture, HAPSMobile Inc., is a Japanese corporation that is 95 percent funded and owned by Japan-based telecommunications operator SoftBank Corp. and 5 percent funded and owned by AeroVironment.
The solar-powered Helios in flight.(Photo: NASA)
AeroVironment is committed to contribute $5 million in capital for its 5 percent ownership of the joint venture, and has an option to increase its ownership stake in HAPSMobile up to 19 percent at the same cost basis as its initial 5 percent purchase.
“This is a historic moment for AeroVironment. For many years, we have fully understood the incredible value high-altitude, long-endurance unmanned aircraft platforms could deliver to countless organizations and millions of people around the world through remote sensing and last mile, next generation IoT connectivity,” said Wahid Nawabi, AeroVironment chief executive officer.“We were searching for the right strategic partner to pursue this very large global opportunity with us.Now we believe we are extremely well-positioned to build on the decades of successful development we have performed to translate our solar UAS innovations into long-term value through HAPSMobile Inc. Our entire team is excited, and we look forward to transforming this strategic growth opportunity into reality.”
AeroVironment pioneered the concept of high-altitude solar-powered UAS in the 1980s, and developed and demonstrated multiple systems for NASA’s Environmental Research Aircraft and Sensor Technology, or ERAST program, in the late 1990s and early 2000s.
In August 2001, the AeroVironment Helios prototype reached an altitude of 96,863 feet, setting the world-record for sustained horizontal flight by a winged aircraft.
In 2002, the AeroVironment Pathfinder Plus prototype performed the world’s first UAS telecommunications demonstrations at 65,000 feet by providing high-definition television (HDTV) signals, third-generation (3G) mobile voice, video and data and high-speed internet connectivity.
Multiple U.S. government agencies funded the development of the hybrid-electric Global Observer unmanned aircraft system from 2007 through 2011. Global Observer represents a solution for extended operation over high northern and southern latitudes during local winters, when the sun’s energy is insufficient to maintain continuous solar aircraft operation at high altitude.
SoftBank Corp. and AeroVironment, Inc. have agreed to license certain background intellectual properties to HAPSMobile, which will own the newly developed UAS intellectual property and possess exclusive rights for commercial applications globally, and non-commercial applications in Japan.AeroVironment will possess exclusive rights to the resulting intellectual property for certain non-commercial applications, except in Japan.AeroVironment will also possess exclusive rights to design and manufacture all such aircraft in the future for HAPSMobile, subject to the terms of the Joint Venture Agreement.
For additional information, please see AeroVironment’s Form 8-K, filed with the Securities and Exchange Commission on Jan. 3.
SkyX Systems Corporation has successfully completed an unmanned data-collection flight of 100 kilometers (km), one of the longest journeys in its class.
The firm flew its SkyOne unmanned aerial system (UAS) on an autonomous data mission over more than 100 km of gas pipeline in Mexico. The robotic flight was programmed and monitored remotely from the company’s Greater Toronto Area SkyCenter mission control, with a support crew of engineers on the ground in Mexico.
Using high-resolution imagery, the longest of multiple flights identified more than 200 potentially significant anomalies along the remote pipeline, ranging from unauthorized buildings and cultivation, to a fissure possibly caused by seismic activity.
More than $38 billion is spent annually monitoring oil and gas pipelines using less efficient means. The SkyX System flight gathered data in a little more than an hour that would have taken a person well over a week. It identified more than 200 georeferenced anomalies the customer was unaware existed, pinpointing precise coordinates for rapid investigation and remediation.
The SkyX System consists of a vertical takeoff and landing (VTOL) drone, the SkyCenter control room, which allows for real-time and secure mission monitoring from remote locations, as well as the company’s proprietary SkyBoxes that enable SkyOne to recharge and continue long-range missions without having to return to home, a factor that limits many drones.
Using the system, a client doesn’t need a trained pilot to operate a remote-control unit — the entire mission is programmed and carried out autonomously, from takeoff to landing. Plus, the VTOL drone eliminates the need for runways, launchers or capture devices.
GNSS testing solutions company Spirent Communications has added BeiDou Phase 3 signals to its GNSS RF constellation simulators.
BeiDou Phase 3 signals are available immediately on the GSS7000 and GSS9000 simulators, and existing users can obtain the software upgrade by contacting Spirent.
The addition of these new signals to the GSS7000 and GSS9000 simulators follows the launch of the first two Beidou-3 satellites in November 2017. Two others were launched Jan. 12.
Phase 3 of the Chinese BeiDou system will extend its coverage from Asia to the entire world. It will provide receiver developers and integrators with additional GNSS signals to make positioning, navigation and timing systems more accurate, and help to support new applications, such as autonomous vehicles.
The new signals will use the same carrier frequencies as the GPS and Galileo systems, so chipset manufacturers and device developers will need to test integrated designs to avoid problems caused by confusing data from different GNSS.
“By offering the BeiDou Phase 3 signals, our customers can test their designs well before the system is fully operational, which is expected in 2020,” said Stuart Smith, lead product manager at Spirent Communications. “With signals already starting to appear, it is important for developers to have test tools that can ensure devices will successfully make use of all GNSS signals.”
