SBG Systems has released the Ellipse 2 Micro series, a new product range designed to reduce the size and cost of high-performance inertial sensors for volume projects. The Ellipse 2 Micro series is available as an inertial measurement unit (IMU), or as an attitude and heading reference system (AHRS) or inertial navigation system (INS) running an extended Kalman filter.
The new Ellipse 2 Micro is available as an IMU for calibrated sensor data, or as an AHRS/INS delivering accurate orientation and navigation using an external GNSS receiver.
The Ellipse 2 Micro series provides excellent navigation data when connected to an external GNSS receiver. The INS fuses in real-time inertial and GNSS information to maintain the vehicle position in air, marine or land applications. For automotive projects, the inertial sensor comes with CAN protocol and connects to the odometer for higher performance in harsh environments, such as tunnels and urban canyons.
“With the Ellipse 2 Micro, integrators benefit from SBG Systems high expertise in motion sensing and positioning in the smallest package,” said Alexis Guinamard, CTO of SBG Systems.
The high-quality micro IMU is calibrated from -40 degrees to 85 degrees Celsius. Combining state-of-the-art MEMS-based gyroscopes, accelerometers and magnetometers, the new Ellipse 2 Micro series is fully calibrated in temperature to eliminate measurement errors such as sensor bias, gain, linearity, alignment and g-sensitivity to provide a constant behavior in all conditions.
Weighing 10 grams, the Ellipse 2 Micros provide a 0.1 degree accurate attitude and connects to external GNSS for navigation, offering a remarkable weight/performance ratio to integrators.
All Ellipse 2 Micro models are now available for order. Product and pricing information is available from SBG Systems representatives and authorized dealers.
ComNav Technology has introduced the T300 Plus GNSS receiver, an upgrade to its T300 receiver.
Designed for demanding surveying tasks, the rugged T300 Plus GNSS features full-constellation tracking capability, tilt compensation, 4G/Wi-Fi connection, 8-GB internal memory and an easy survey workflow with Android-based Survey Master Software. It is designed to make collecting accurate data easy and fast, whether done by a beginner or experienced professional surveyor.
As an upgrade of the T300, SinoGNSS T300 Plus combines a GNSS board, Bluetooth and adjustable TX/RX UHF, Wi-Fi and 4G modem into one rugged device. Its built-in 4G modem ensures the T300 Plus works with all kinds of CORS worldwide. Moreover, T300 Plus built-in tilt sensor supports maximum 30-degree pole tilt and keeps the compensation accuracy within 3 centimeters, and the user can check the electronic bubble on the controller for fast surveys in the field.
With two hot-swappable batteries, the T300 Plus helps to extend working hours, the company said. The battery LEDs flash when the battery runs low; they can be replaced or charged directly while in the T300 Plus through an external power connection. Its consumer-grade battery design is compatible with the Canon LP-E6, which can be purchased in retail stores.
https://youtu.be/HddhJXF8D3Y
ComNav Technology also provides the T300 Plus GNSS surveying system with the Android-based R500 data collector and Survey Master software, providing for powerful and straightforward survey workflow in the field.
The T300 Plus is now available through ComNav Technology authorized local distributors or directly through ComNav Technology.
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.
At Intergeo 2017, Juniper System Ltd.’s Simon Bowe gives GPS World a rundown on two of the company’s latest products: the Mesa 2 tablet and Geode real-time sub-meter GNSS receiver. Learn about the features of the two products.
A new report describes the benefits growers can experience using NovAtel’s TerraStar-C and TerraStar-L precise point positioning (PPP) technology with a SMART6-L GNSS receiver for automatic steering.
The report, “DLG Test Report 6802, NovAtel SMART6L receiver PPP Automatic Steering Test,” was issued by the German Agricultural Society or Deutsche Landwirtschafts-Gesellschaft (DLG).
DLG promotes technical and scientific progress in the agriculture and food sectors. Set up to test equipment and machinery, the DLG Test Center Machinery and Farm Inputs provide impartial information for practitioners.
DLG awards a DLG-APPROVED quality mark to farm machinery that passes a limited test programme within a DLG usability test. Using a Fendt 828 Vario tractor, combined with the Fendt “VarioGuide RTK” steering system and the NovAtel SMART6-L receiver, steering accuracy was tested using both TerraStar-C and TerraStar-L correction services.
NovAtel’s TerraStar-C correction service provides a 5 cm or less (95%) position accuracy, and TerraStar-L provides a 50 cm or less (95%) position accuracy with a 15-cm pass-to-pass accuracy. The DLG test is designed to measure steering accuracy under various conditions as part of the criteria to receive the DLG-APPROVED quality mark.
The DLG test reported that NovAtel’s TerraStar-C correction service achieved 2-3 cm system steering accuracy on an A-B run on an even track at 8 km/h, and a 3.4 cm accuracy at 15 km/h. The long-term accuracy was reported at 3-4 cm over a 24-hour period. Also, the pass-to-pass error was less than 3 cm over the duration of an hour.
The DLG test reported NovAtel’s TerraStar-L correction service at 17-18 cm accuracy during an hour A-B run on an even track at 8 km/h, and 10-11 cm accuracy on a contour run at 5 km/h. The pass-to-pass error remained below 15 cm for the duration of the tests.
NovAtel’s SMART6-L was awarded the DLG-APPROVED quality mark after successfully completing the PPP autosteering test.
NVS Technologies provides a company overview, as well as a look at its latest GNSS receivers and modules, at Intergeo 2017, which took place Sept. 26-28 in Berlin, Germany.
Sokkia has unveiled a new radio modem designed to offer advanced radio connectivity with GNSS receivers. The R4S-BT UHF radio provides an external option for use with the Sokkia GCX receiver line.
The UHF multichannel radio modem has a tuning range of up to 70 MHz. Additionally, the radio features an IP67 certified housing with internal batteries that is designed to be easy to carry with versatile mounting options.
“The R4S-BT makes the GCX GNSS receiver into an even more scalable and modular solution,” said Jason Hallett, vice president of global product management at Sokkia. “It is perfect in situations without a network connection or when long-range Bluetooth technology is not enough on its own. Survey and mapping professionals can simply add on this external UHF and extend the range between the base and rover.”
Connectivity options include wireless data transfer and USB connections.
The use of GPS signals is certainly commonplace in today’s technological age. Various locating systems, tracking systems and precision timing applications all use the common decoded NMEA and 1 PPS signals from a GPS satellite in a multitude of different ways.
When a direct line-of-sight path to GPS satellites is unavailable, the GPS signal must first be received where there is a direct line-sight path, decoded, and then the resulting signals routed to where they are needed. The Luxlink GPSX-1001 has been designed to do exactly that.
LuxLink GPSX-1001 fiber-optic transceiver.
The GPSX-1001 is the result of a specific request by a research group of a midwestern U.S. university for seismic studies in an underground mine. More than 20 units were installed in several branches of the mine and have been in continuous operation successfully for two years.
The GPSX-1001 transceiver is a multifunctional device that can be used as a transmitter or a receiver/repeater. In operation, the NMEA signal and the 1 PPS signal are both multiplexed by the GPSX-1001 (set as a transmitter) and launched into a single optical fiber. The multiplexed signal is then received from the fiber at a second GPSX-1001 set as a receiver/repeater. Here, the NMEA and 1PPS signal are de-multiplexed and available as individual outputs (see Figure 1).
FIGURE 1. GPSX-1001 block diagram.
The original multiplexed signal is also then reapplied to another integral optical transmitter for use at a third receiver/repeater. Additional receiver/repeaters can be connected in the same fashion to allow the signals to be transmitted to numerous locations.
Fiber-optic cable is virtually immune to electrical interference and can be routed wherever convenient without regard to the proximity of electrical noise producers, water or high voltages. Because fiber optic cable is non-conducting, ground loops that can result in loss or corruption of the GPS signals are virtually eliminated. The bandwidth of the fiber and circuitry in the GPSX-1001 is such that the fast rise and fall times of the 1-PPS signal are maintained and the NMEA signal is as noise free as the original input.
Transmission distances using the GPSX-1001 can extend to a mile or more. For longer distances, additional GPSX-1001 units can be added.
The GPSX-1001 is user configured by means of front-panel DIP switches. Integral LED indicators are provided to continuously monitor the NMEA, 1 PPS, power and optical link signals. Power is obtained from simple wall type plug-in adapters or low voltages and need not be regulated because the GPSX-1001 units contain internal regulators.
Figure 2 shows three GPSX-1001 units in a typical GPS signal distribution system. The NMEA interface can be RS-422 or RS-232, depending on the requirements of the signal source. The 1 PPS signal is 50-ohm TTL compatible. Each transceiver pair will produce signals over distances in excess of several miles and will operate from –35° to +75° C (–31° to 167° F), allowing them to be used both indoors and outdoors. Units are available for use with multimode or single-mode fiber and with standard fiber-optic connectors.
FIGURE 2. GPS NMEA/1 PPS transmission system.
Irwin Math is president of Liteway Inc. and has more than 30 years of experience in the design and development of fiber-optic transmission systems. He was also the founder of Math Associates Inc., one of the pioneering firms in fiber-optic transmission system technology in the early 1980s.
Broadcom Limited is offering a mass-market, dual-frequency GNSS receiver device, the BCM47755, designed to enhance location-based services (LBS) applications for mobile phones, tablets and fitness wearables.
Equipped with the latest GNSS innovations, the device is capable of centimeter-level accuracy with minimal power consumption and footprint, enabling an entirely new suite of high-precision LBS applications including lane-level vehicle navigation and mobile augmented reality.
Until now, mobile location based applications have been powered by single-frequency GNSS receivers operating under stringent battery-power and footprint constraints.
The expanded availability of L1/E1 and L5/E5 frequencies in satellite constellations enables the use of two frequencies to compute position much more accurately in both urban and open area environments.
BCM47755 uses two different frequency signals from each satellite. (Image courtesy of Broadcom)
The BCM47755 delivers this higher level of location accuracy while meeting the rigorous battery power and footprint needs in mobile phones.
The BCM47755’s accuracy allows location-based applications to offer a richer consumer experience. For example, lane-level knowledge of the vehicle’s location vastly improves the turn-by-turn navigation performance.
Further, combining this accurate location with the lane’s traffic pattern gives consumers a significantly better estimate of arrival times. In the same vein, ride-sharing applications can be enhanced to more precisely pinpoint driver and client location.
The BCM47755 consumes less than half the power of previous generation GNSS chips. Since GNSS and sensor applications are always on, this power efficiency has a proportional impact on the battery life of the mobile device. So, even while benefiting from a richer navigation experience, consumers will have a longer lasting battery on mobile devices that use the BCM47755.
Product Highlights
Advanced dual-frequency GNSS receiver capable of processing satellite signals in both L1/E1 and L5/E5 frequency bands providing higher level of location accuracy
Incorporates new low power GNSS radio and dual-core ARM CM4-CM0 sensor hub
More than 50 percent lower power consumption compared to previous generation GNSS receiver
Delivers high-quality raw GNSS measurements for both code and carrier phase, enabling advanced location-based applications.
“With the launch of the dual-frequency GNSS sensor hub, Broadcom continues the tradition of raising the bar for mobile GNSS,” said Vijay Nagarajan, senior director of product marketing of the Mobile Connectivity Products Division at Broadcom. “Location-based consumer applications can be disruptively enhanced with centimeter-level accuracy. On the other hand, lower power consumption and smaller footprint continue to be defining requirements for any mobile phone chip. The BCM47755 achieves these twin objectives for a richer consumer experience.”
Tersus GNSS Inc. has released a major upgrade to its Precis-BX306 RTK board with new and improved GPS/GLONASS functionality. Tersus GNSS is a manufacturer of high-precision GNSS real-time kinematic (RTK) boards, receivers and systems.
Precis-BX306 Board Easy Kit.
The new version of Precis-BX306 supports up to 20-Hz RTK solution and raw measurement output, which can be integrated with autopilots and inertial navigation units.
With improved algorithms, the new Precis-BX306 demonstrates its ability that the 30-km baseline can be fixed quickly, the company said.
Moreover, the dynamic fix rate shows an advantage comparing to the majority of competitive solutions. Stable fix rate is achieved when it is working under city valley, tree, and other challenging environment.
“The technology changes made in this version give a whole new user experience for our customers,” said Xiaohua Wen, founder and CEO. “With the updated capabilities, the Precis-BX306 is particularly useful for drones, surveyors and geographic information system data users interested in an affordable RTK solution.”
This latest version of Precis-BX306 is pin-to-pin compatible with major GNSS boards in the market, offering a flexible interface. Event mark and PPS are supported as always. All enhanced features demonstrate Tersus’ commitment to the needs of customers who value dynamic accuracy and stability.
The new Precis-BX306 is available for order and delivery immediately, and it will be shown at Intergeo in Berlin, Germany, Sept. 26-28.
Unicore has launched its next-generation quad-system GNSS module, the UM482.
The UM482 is a multi-frequency high-precision heading module with a small footprint, supporting the satellite signals BDS B1/B2, GPS L1/L2, GLONASS L1/L2, Galileo E1/ E5b and SBAS.
The module is designed for applications such as robotics, drones, intelligent drives and mechanical control.
1-cm RTK positioning accuracy and 0.2-degree heading accuracy with 1-m baseline
Dual antenna input with support of antenna signal detection
Supporting simultaneous output of heading and positioning, 20-Hz data output rate
Adaptive recognition of RTCM input data format
On-board micro-electro-mechanical system (MEMS) integrated navigation
The UM482 GNSS RTK module adopts Unicore’s new-generation Nebulas II chip and UGypsophila real-time kinematic (RTK) algorithm.
Based on high performance data-sharing technology and the simplified operation system of the Nebulas II chip, the UGypsophila RTK algorithm dramatically optimizes matrix processing, the company said. It can involve all satellites from GPS, BDS, GLONASS and Galileo in RTK and heading processing, shorten RTK and heading initialization time to 5 seconds and significantly improve the reliability and accuracy of RTK and heading.
Furthermore, the UM482 integrates the onboard MEMS chip and U-Fusion integrated navigation algorithm, resulting in optimized continuity and reliability of accurate heading and positioning output in tough environments such as city canyons, tunnels and overpasses. Inputs of odometer and external higher performance inertial components are supported.
The UM482, along with all the UM and UB family of receivers, will be on display at booth B4018 for the duration of the Intergeo 2017 trade show, which takes place Sept. 26-28 at Berlin Exhibition Center, Berlin, Germany.
Even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
By Robert Odolinski and Peter J.G. Teunissen
ALL GOOD THINGS ARE CHEAP; ALL BAD ARE VERY DEAR. That’s what the famous American essayist (and surveyor) Henry David Thoreau wrote in his diary on March 3, 1841. He was likely referring, in part, to the cheapness of the things he came across in nature such as birdsong or the plants and trees on the shores of Walden Pond and the dearness of some luxuries and comforts of civilization, which he tended to eschew. But what has that got to do with GPS, you might ask?
When they were first introduced in the late 1970s and early 1980s, GPS receivers were very dear. Many of them sold for anywhere from $50,000 to $250,000, which would be equivalent to about twice those amounts in today’s dollars. The first civilian receivers were large bulky affairs. As I documented in this column in April 1990 (“Smaller and Smaller: The Evolution of the GPS Receiver”), the “first commercially available GPS receiver was the STI-5010 built by Stanford Telecommunications Inc. It was a dual-frequency, C/A- and P-code, slow-sequencing receiver. Cycling through four satellites took about five minutes, and the receiver unit alone required about 30 centimeters of rack space. External counters, also requiring rack space, made pseudorange measurements. An external computer controlled the receiver and computed positions.” While it could be transported in a small truck (and some were), it was not designed for portability and ease of use by surveyors or geodesists.
Then, in 1982, Texas Instruments introduced the first relatively compact civil GPS receiver, the TI 4100, also known as the Navstar Navigator. And as I also noted in that column more than 15 years ago, this “receiver could make both C/A- and P-code measurements along with carrier-phase measurements on both L1 and L2 frequencies. Its single hardware channel could track four satellites simultaneously through a multiplexing arrangement. The 37 × 45 × 21-centimeter receiver/processor had a handheld control and display unit and an optional dual-cassette data recorder for saving measurements for post-processing. The unit, although portable, weighed 25 kilograms and consumed 110 watts of power (the receiver doubled as a hand warmer). Field operation required a supply of automobile batteries.”
My, how things have changed. Beginning around 1990, receivers steadily got smaller and smaller and cheaper and cheaper. Survey-grade GNSS (not just GPS) receivers can now be purchased for well under $10,000 and consumer-grade units sell for as little as a hundred dollars or less. And, of course, the GNSS modules inside smartphones and other devices cost manufacturers only a couple of dollars or so.
But even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
Read on to find out.
GPS has been the number-one positioning tool for a range of applications during the past few decades. The integration of the emerging global navigation satellite systems, such as the Chinese BeiDou Navigation Satellite System (BDS), can give improved precise (millimeter- to centimeter-level) real-time kinematic (RTK) positioning. When BDS is combined with GPS, about double the number of satellites are visible in the Asia-Pacific region, which can make single-frequency RTK and low-cost receiver RTK positioning possible.
In this article, we will analyze the performance of L1 GPS + B1 BDS in Dunedin, New Zealand, using low-cost receivers. We compare their performance to that of L1+L2 GPS survey-grade receivers.
First, we describe the GPS+BDS functional and stochastic models and the data used for our evaluations. Least-squares variance component estimation (LS-VCE) is used as a means to determine the code and phase (co)variances to formulate a realistic stochastic model. (An incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions.)
Having correctly defined the stochastic model, we focus on the positioning performance. We investigated the ambiguity resolution and positioning performance, both formally and empirically, for customary and high-elevation cut-off angles. The high cut-off angles are used to mimic situations when low-elevation multipath is to be avoided. Lastly, we compared all our results between using low-cost and survey-grade antennas.
GPS+BDS POSITIONING MODEL
The model that we used for positioning is given as follows. Assume that sG + 1 GPS satellites are tracked on fG frequencies and sB + 1 BDS satellites on fB frequencies. As we apply system-specific double-differencing (DD), one pivot satellite is used per system. The total number of DD phase and code observations per epoch then equals 2 fGsG + 2 fBsB. We assume for now that cross-correlation between frequencies as well as code and phase is absent. The combined multi-frequency short-baseline GPS+BDS model is then defined as follows.
The system-specific DD phase and code observation vectors are denoted as φ* and p*, respectively, with * = {G, B} where G = GPS and B = BDS. The single-epoch GNSS model of the combined system is given as
(1)
and
(2)
in which
is the combined phase vector,
is the combined code vector,
is the combined integer ambiguity vector,
is the real-valued baseline vector,
is the combined phase random observation noise vector,
is the combined code random observation noise vector, and
D[.] denotes the dispersion operator.
The entries of the baseline design and wavelength matrices are given as
where is the x 1 vector of 1s, is the differencing matrix, is the unit matrix, the geometry-matrices GG and GB contain the undifferenced receiver-satellite unit direction vectors for GPS and BDS, respectively, is the wavelength of frequency , denotes the Kronecker product, and “diag” and “blkdiag” indicate diagonal and block diagonal matrices, respectively. The entries of the positive definite variance matrices are given as
(3)
where , denote the phase and code standard deviation, respectively, and the satellite elevation-angle-dependent weight.
The model in Equation 1 applies to short baselines, and thus the ionospheric and tropospheric delays are assumed absent. The broadcast ephemerides are used to obtain the satellite coordinates. Further, the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) technique is used to estimate the integer ambiguities a. The observation noise vectors εand e, respectively, are zero-mean vectors, provided that no multipath is present in Equation 1.
EXPERIMENT SETUP
The GNSS receivers we used are depicted in FIGURE 1. Firstly, two low-cost single-frequency receivers were set up to collect L1+B1 GPS+BDS data for two days. These receivers cost a few hundred U.S. dollars. Since the patch antennas we used have been shown to have less effective signal reception and multipath suppression in comparison to survey-grade antennas, the receivers that collected data for two days were additionally connected to such antennas. These antennas have a cost of slightly more than US$1,000 per antenna. To compare the low-cost solution to a survey-grade receiver-solution, two such receivers (which cost several thousand U.S. dollars) were connected to the same survey-grade antennas through splitters and collected L1+L2 GPS data. A detection, identification and adaption procedure was used to eliminate any outliers.
FIGURE 1. Low-cost single-frequency receivers collecting GPS+BDS data for single-baseline RTK, with patch antennas (left) and survey-grade antennas (right) on Jan. 4–6 and Jan. 6–8, 2016, respectively. Survey-grade dual- frequency GPS receivers were connected to the same survey-grade antennas simultaneously to truly track the same GPS constellation.
FIGURE 2 depicts the corresponding redundancy of the two receiver models (that is, the number of observations minus the number of estimated unknowns) together with the number of satellites over 48 hours (30-second epoch interval). The number of BDS satellites (magenta lines) is overall smaller than when compared to GPS (blue lines) in Dunedin. However, Figure 2 also shows that the model strength of L1+B1 GPS+BDS, as measured by its redundancy, is almost similar to that of L1+L2 GPS except for some hours at the middle of the two days. This implies that the two receiver models can potentially give competitive RTK ambiguity resolution and positioning performance. This is however only true if the receiver code and phase observation noise would be of similar magnitude between the receivers used, hence the need for an analysis of the receiver observation precision.
FIGURE 2. Redundancy (left) and number of satellites (right) of L1+B1 GPS+BDS and L1+L2 GPS during Jan. 6–8, 2016, (48 hours) for an elevation cut-off angle of 10°.
In our receiver evaluations, we determined a set of reference ambiguities by using a known baseline and treating them as time-constant parameters over the two days in a dynamic model.
LOW-COST RTK POSITIONING
The code and phase variances were estimated by LS-VCE using data independent from the data used for the following positioning analysis. The variances are needed to formulate a realistic stochastic model, whereas an incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions. TABLE 1 depicts the corresponding estimated standard deviations (STDs) used for our positioning models.
TAB LE 1. Zenith-referenced undifferenced code and phase standard deviations estimated by least-squares variance component estimation.
Table 1 shows that the code precision of L1 GPS and B1 BDS improves significantly when the survey-grade antennas are used instead of patch antennas (49 centimeters STD for L1/B1 that decreases to about 30 centimeters), due to their better signal reception and multipath suppression abilities. For testing our stochastic model, we used data that is independent from the data used to estimate the code/phase precision.
Positioning Performance. The single-epoch (instantaneous) RTK positioning results for 24 hours data are shown in FIGURE 3, with ambiguity-float solutions shown at the top and ambiguity-fixed solutions at the bottom. Only the correctly fixed solutions are depicted as determined by comparing the instantaneously estimated ambiguities to the set of reference ambiguities. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. They were computed from the empirical and formal position variance matrices. The empirical variance matrix was estimated from the positioning errors as obtained from comparing the estimated positions to precise benchmark coordinates. The formal variance matrix used was determined from the mean of all single-epoch formal variance matrices.
FIGURE 3. Horizontal (north (N), east (E)) position scatter and corresponding vertical (U) time series of the float (top) and correctly fixed (bottom) L1+B1 GPS+BDS single-epoch RTK solutions for an elevation cut-off angle of 10°. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. The 24 hour (30 second) period is 22:00-22:00 UTC Jan. 5-6, 2016, for patch antennas in (a) and 21:48-21:48 UTC Jan. 8-9, 2016, for survey-grade antennas in (b), which are periods independent of the periods used to determine the stochastic model through the code/phase STDs in Table 1.
Figure 3 shows a good fit between the formal and empirical confidence ellipses/intervals, which thus illustrates realistic LS-VCE STDs in Table 1 that were used in the stochastic model. Note also the two-order of magnitude improvement when going from float to fixed solutions, and that the low-cost receiver plus survey-grade antenna has the most precise ambiguity-float positioning solutions.
Ambiguity Resolution and Positioning Performance for Higher Cut-Off Angles. We subsequently investigated the low-cost L1+B1 GPS+BDS performance for high elevation cut-off angles, so as to mimic situations in urban canyon environments or when low-elevation-angle multipath is present and is to be avoided. We have made comparisons to the survey-grade L1+L2 GPS results. It has been shown that a good ambiguity resolution performance does not necessarily imply a good positioning performance, so we investigated what effect this has on our positioning models.
The following integer least-squares (ILS) success rates (SRs) are thus computed based on epochs with the condition of positional dilution of precision (PDOP) ≤ 10 and averaged over all epochs over two days of data. By including and excluding epochs with large PDOPs, we can show how the positioning performance of the different models is affected by poor receiver-satellite geometries. To better understand how this exclusion of epochs with large PDOPs also influenced the empirical ambiguity-correctly-fixed positioning performance, we constructed TABLE 2, which shows the corresponding positioning STDs for two days of data. These STDs were computed by comparing the estimated positions to precise benchmark coordinates. In addition to the positioning performance, we depict in Table 2 the corresponding empirical ILS SR for full ambiguity-resolution, which is given by the ratio of the number of correctly fixed epochs to the total number of epochs.
TABLE 2. Single-epoch empirical STDs (N, E, U) of correctly fixed positions for the three positioning models together with their ILS SR for four elevation cut-off angles and 48 hours of data (Jan. 4–6 and Jan. 6–8, 2016). The empirical STDs and ILS SRs are also shown when conditioned on PDOP ≤ 10.
Table 2 shows that the L1+B1 low-cost receiver plus patch antenna combination has (as expected) smaller SRs in comparison to those when the survey-grade antenna is used. This latter combination has comparable SRs to the (PDOP-conditioned) SRs of the survey-grade L1+L2 GPS receiver for cut-off angles up to 25°.
In support of better understanding Table 2, FIGURE 4 shows typical positioning results for the different receiver and antenna combinations with elevation cut-off angles of 10° (top two rows) and 25° (bottom two rows). The first and third rows show the local horizontal (N, E) positioning scatterplots and the second and fourth rows the vertical (U) time series over two days of data. The float solutions are depicted in gray, and incorrectly and correctly fixed solutions in red and green, respectively. The zoom-in is given to better show the spread of the correctly fixed solutions with millimeter-centimeter level precisions. The formal ambiguity-float STDs are also shown under the up time series to reflect consistency between the empirical and formal positioning results.
FIGURE 4. Horizontal (N, E) scatterplots and vertical (U) time series for L1+B1 low-cost receiver with patch antenna (first column) with 99.5% (89.8%) ILS SR, L1+B1 low-cost receiver with survey-grade antenna (second column) with 100% (97.8%) ILS SR, and survey-grade L1+L2 GPS (third column) with 100% (94.1%) ILS SR, using 10° (top two rows) and 25° (bottom two rows) cut-off angles respectively (Jan. 4–6, 2016, for low-cost receiver with patch antenna and Jan. 7–8, 2016, for the low-cost and survey-grade receivers with survey-grade antennas). The SRs are conditioned on PDOP ≤ 10 and computed based on all epochs. Below the vertical time series, the ADOP is depicted in blue color, the 0.12-cycles level as red, and ambiguity-float vertical formal STDs are shown in gray.
We also depict in Figure 4 the ambiguity dilution of precision (ADOP) as an easy-to-compute scalar diagnostic to measure the intrinsic model strength for successful ambiguity resolution. The ADOP is defined as
(cycles) (4)
with n being the dimension of the ambiguity vector, the ambiguity variance matrix, and |.| denoting the determinant. ADOP gives a good approximation to the average precision of the ambiguities, and it also provides for a good approximation to the ILS SR. The rule-of-thumb is that an ADOP smaller than about 0.12 cycles corresponds to an ambiguity SR larger than 99.9%.
Figure 4 shows that more solutions are incorrectly fixed (red dots) when the ADOPs (blue lines) are larger than the 0.12 cycle level (red dashed lines). The figure also reveals that the L1+B1 low-cost receiver plus patch antenna combination achieves an ILS SR (99.5%) similar to that of the survey-grade L1+L2 GPS receiver (SR of 100%) for the cut-off angle of 10°. This ILS SR corresponds to the availability of correctly fixed solutions (green dots) with millimeter-centimeter level positioning precision over the two days. The L1+L2 GPS receiver has, moreover, large ambiguity-fixed positioning excursions at the same time as the formal STDs are large for the cut-off angle of 25° due the poor GPS-only receiver-satellite geometry for this high cut-off angle. This is also reflected by the corresponding relatively large ambiguity-fixed STDs depicted in Table 2 that are improved from decimeter- to millimeter-level when the PDOP ≤ 10 condition is applied. Figure 4 also shows that the L1+B1 low-cost receiver with the survey-grade antenna has a larger SR of 97.8% when compared to the PDOP-conditioned SR for L1+L2 GPS of 94.1% for the cut-off angle of 25° (see also Table 2), owing to the use of BDS that significantly improves the receiver-satellite geometry.
Finally, we also tested the low-cost receiver-solution (with survey-grade antennas) for a baseline length of 7 kilometers, where (small) residual slant ionospheric delays are present. It was shown that this combination still has the potential to achieve ambiguity resolution and positioning performance competitive with the survey-grade receiver-solution.
CONCLUSIONS
In this article, we evaluated a low-cost L1+B1 GPS+BDS RTK setup and compared its ambiguity resolution and positioning performance to a survey-grade L1+L2 GPS solution in Dunedin, New Zealand. The LS-VCE procedure was used to determine the variances of the low-cost receivers. The estimated variances are needed so as to formulate a realistic stochastic model, otherwise the ambiguity resolution and hence the achievable positioning precisions would deteriorate.
Since we analyzed a short baseline, the LS-VCE variances were shown to likely be affected by multipath. To mitigate multipath we connected the low-cost receivers to survey-grade antennas with better signal reception and multipath suppression abilities. It was shown that the survey-grade antennas can significantly improve the performance for the low-cost receivers so that the code/phase noise estimates more resemble that of survey-grade receivers. The LS-VCE STDs were furthermore shown to be realistically estimated for an independent time period.
We also demonstrated that the low-cost receivers can give competitive instantaneous ambiguity resolution and positioning performance to that of the survey-grade receivers. This is particularly true when the low-cost receivers are connected to survey-grade antennas.
ACKNOWLEDGMENTS
This article is based on the paper “On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” presented at the 2017 International Technical Meeting of The Institute of Navigation held Jan. 30-Feb. 1, 2017, in Monterey, California.
Ryan Cambridge at the School of Surveying, University of Otago, collected the low-cost receiver data. Author Peter J.G. Teunissen was supported by an Australian Research Council Federation Fellowship. All of this support is gratefully acknowledged.
MANUFACTURERS
The low-cost receivers used in the research were u-blox EVK-M8T receivers. The survey-grade receivers were Trimble NetRS receivers. The patch antennas were u-blox ANN-MS antennas, while the survey-grade antennas were Trimble Zephyr 2 GNSS antennas.
ROBERT ODOLINSKI conducted his Ph.D. studies at Curtin University, Perth, Australia, from 2011 to 2014. His research focus is next-generation multi-GNSS integer ambiguity resolution enabled precise positioning. In 2015, Odolinski started his position as a lecturer/research fellow in geodesy/GNSS at the School of Surveying, University of Otago, New Zealand.
PETER J.G. TEUNISSEN is a professor of geodesy and navigation and the head of the Curtin GNSS Research Centre, Curtin University. He is also with the Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands. His research interests include multiple GNSS and the modeling of next-generation GNSS for high-precision positioning, navigation and timing applications.
FURTHER READING
• Authors’ Conference Paper
“On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” by R. Odolinski and P.J.G. Teunissen in Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 30 – 1 Feb., 2017, pp. 745–753.
• Authors’ Related Work
“Single-Frequency, Dual-GNSS Versus Dual-frequency, Single-GNSS: A Low-cost and High-grade Receivers GPS-BDS RTK Analysis” by R. Odolinski and P.J.G. Teunissen in Journal of Geodesy, Vol. 90, No. 11, 2016, pp. 1255–1278, doi:10.1007/s00190-016-0921-x.
“Combined BDS, Galileo, QZSS and GPS Single-frequency RTK” by R. Odolinski, P.J.G. Teunissen and D. Odijk in GPS Solutions, Vol. 19, No. 1, 2015, pp. 151–163, doi:10.1007/s10291-014-0376-6.
“Instantaneous BeiDou+GPS RTK Positioning With High Cut-off Elevation Angles” by P.J.G. Teunissen, R. Odolinski and D. Odijk in Journal of Geodesy, Vol. 88, No. 4, 2014, pp. 335–350, doi: 10.1007/s00190-013-0686-4.
“The Future of Single-Frequency Integer Ambiguity Resolution” by S. Verhagen, P.J.G. Teunissen and D. Odijk in Proceedings of the VII Hotine-Marussi Symposium on Mathematical Geodesy, Rome, June 6–10, 2009, edited by N. Sneeuw, P. Novák, M. Crespi and F. Sanso, International Association of Geodesy Symposia, Vol. 137, 2012, pp. 33–38, doi:10.1007/978-3-642-22078-4 5.
“Centimeter-Level Positioning for UAVs and Other Mass-Market Applications” by C. Mongredien, J.-P. Doyen, M. Strom and D. Ammann in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 1441–1454.
“Initial Assessment of the COMPASS/BeiDou-2 Regional Navigation Satellite System” by O. Montenbruck, A. Hauschild, P. Steigenberger, U. Hugentobler, P.J.G. Teunissen and S. Nakamura in GPS Solutions, Vol. 17, No. 2, 2013, pp. 211–222, doi:10.1007/s10291-012-0272-x.
• LAMBDA
“On the Reliability of Integer Ambiguity Resolution” by S. Verhagen in Navigation, Vol. 52, No. 2, Summer 2005, pp. 99–110, doi: 10.1002/j.2161-4296.2005.tb01736.x.
“ADOP in Closed Form for a Hierarchy of Multi-frequency Single-baseline GNSS Models” by D. Odijk and P.J.G. Teunissen in Journal of Geodesy, Vol. 82, 2008, pp. 473–492, doi: 10.1007/s00190-007-0197-2.
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