Artists rendering of the B-21 raider, which is being produced by Northrup Grumman for the U.S. Air Force to operate in tomorrow’s high-end threat environment. (Image: U.S. Air Force)
With assured positioning, navigation and timing (APNT) and low-Earth orbit PNT (LEO PNT) coming on strong, what does the future hold for military and commercial systems dependent on the current configuration of GPS? Should military and commercial platforms be modified to include APNT, for now, with an eye to adding LEO PNT in the future? Should they integrate these two systems, or rely on one or the other as standalone systems?
Government and industry agree that interference with GPS and all GNSS is an increasing threat as jamming and spoofing technologies evolve. This has prompted government support for APNT to bolster GPS. A Feb. 12, 2020, Executive Order required a comprehensive update to national policy on PNT services by the federal government, and by owners and operators of critical infrastructure to strengthen the resilience of critical infrastructure.
Research, development and production have improved the performance — positioning, timing and (desired) accuracy — of GNSS PNT and the ability to operate in RF-challenged environments. APNT gives the U.S. military a reliable way to further enable GPS, or to act as an alternative to it, by utilizing other sensors, such as inertial navigation systems, differential GPS, visual sensors, lidar, radar, radios and star trackers that complement GPS.
The near-term expansion of internet service to include commercial broadband LEO satellites also provides potential for robust PNT, using their waveforms as signals of opportunity (SOOP). GPS and other GNSS have an infrastructure to maintain very precise time throughout their constellations, as well as satellites with specially designed transmitters, clocks, and a waveform dedicated to the PNT function. By contrast, SOOPs are in space for another purpose and not optimized for PNT. Therefore, the challenge is to exploit features of the SOOP waveforms, designing innovative techniques to determine the range to each satellite and to provide users with reliable PNT. The approach for LEO PNT may have applications to ground troops and for aerial, munition, missile and commercial applications requiring higher levels of PNT security and integrity.
GPS receivers for future military platform designs may use a software defined radio (SDR) approach and be capable of incorporating LEO PNT signals. This technology, although designed to work standalone, can be used to complement existing navigation sensors that are typically used in navigation systems, including APNT. Expansion to the usage of multiple constellations will serve to optimize performance and resiliency in an RF-challenged environment. However, LEO satellites’ closer proximity to Earth and their signal structures allow for higher signal powers, thus are more robust against jamming. With all these separate systems or fusion by SDR, how does the receiver ensure the integrity of the signal or its accuracy? An SDR qualification test would involve an unlimited number of scenarios.
One hallmark of the GPS program is that it facilitates a thorough systems engineering effort by managing in a single location interface control documents (ICDs) for alternative systems being developed by different program offices all over the country. This makes both the integration of the systems and the development of the receivers extremely difficult and complex.
“The new SPD-7 [Space Policy Directive 7, the United States Space-based Positioning, Navigation and Timing Policy, dated Jan. 15, 2021] focusing on interoperability and APNT is a seminal document to address a realized threat and a way forward,” said Bernie Gruber, a former head of the GPS Directorate (now the Military Communications and PNT Directorate). “To that end, the combination of SDRs and data fusion potentially offer a clear advantage to utilize signal and sensor diversity, thus improving the robustness of critical PNT information.”
A Step-by-Step Exposition of an Educational Resource
Innovation Insights with Richard Langley
THE RADIO. It’s been around for more than 100 years. Pioneering work by Guglielmo Marconi and others in the1890s and 1900s resulted in practical wireless telegraphy devices that permitted point-to-point communications with ships at sea and between stations on land hundreds and thousands of kilometers apart and even between stations on different continents. The first radio broadcasts (point-to-multipoint) were time signal transmissions and weather broadcasts. Experimental audio transmissions took place in the early 1900s, and by 1920 or so, radio stations were established in many countries for broadcasting speech and music to the general public.
The first radio receivers were simple crystal sets. It wasn’t until the mid-1920s that tube radios became commercially available. Eventually, tubes were replaced by transistors, and transistors by integrated circuits. The introduction of microprocessors resulted in digital receivers, with the conversion of the received analog radio signals into audio being carried out digitally for the most part.
One of the latest advances in radio technology is the software-defined radio or SDR. An SDR typically consists of two components: a piece of hardware, called a radio frequency (RF) front end, and a piece of software run on a general-purpose computer. The job of the front end is to convert a portion of the radio spectrum received by its antenna to a digital data stream processed by the software. The software decodes the data to produce the desired result. Since the software does most of the “heavy lifting” in processing a radio signal, it is often called the SDR itself. And by the way, there are SDR transmitters, too.
It should come as no surprise that SDR technology has come to the GNSS field. In fact, in 2007, the seminal text on GNSS SDRs, A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach, was published along with the sale of an inexpensive RF front-end in a thumb-drive-sized package that allowed graduate students and others to experiment with a GNSS SDR themselves. And we have covered GNSS SDR developments in this column from time to time, most recently in January 2018 (“The Continued Evolution of the GNSS Software-Defined Radio: Getting Better All the Time”).
In this month’s column, researchers from the lab that helped produce the SDRs documented in the 2007 book (which is still in print) discuss their development and testing of additional freely available SDR codebases covering all four GNSS (GPS, Galileo, BeiDou and GLONASS). They provide an excellent resource for learning how GNSS receivers actually work.
By Joan Bernabeu, Nicolas Gault, Yafeng Li and Dennis M. Akos
With the publication of the book A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by Kai Borre, Dennis Akos and their fellow authors, an open-source GNSS software-defined radio (SDR) receiver developed using Mathwork’s Matlab language was made available, together with sample data sets that facilitated the testing process for all interested readers. The first SDR implementation focused on processing the GPS L1 C/A-code legacy signal and served as a starting point for students and researchers in the Radio Frequency (RF) and Satellite Navigation Laboratory at the University of Colorado Boulder, where later activities aimed to improve the software code and add new features as new GNSS signals emerged. As a result, the initial codebase evolved into a complete collection of SDRs capable of processing all GNSS signals from every satellite constellation, with BeiDou’s B1I, B1C, B3I and B2a signals the latest additions. The most recent efforts were dedicated to collecting all SDR codebases, putting them in a common format, and testing them to give an account of their performance. This article describes our efforts, placing special emphasis on explaining the test framework designed to test each SDR, as well as on reporting the adjustments made and the results obtained. GPS test cases have been taken as examples to show how some SDRs were assessed when issues were found in the results they provided.
OPEN-SOURCE GNSS SDR COLLECTION
The whole SDR collection has been developed in Mathwork’s Matlab programming language. To run the code and perform tests, users simply require an active Matlab license and the software available on their computer. Once these requirements are met, the user can choose to download any of the available codebases and the corresponding data set to start experimenting.
We recommend using version control software to keep track of changes made to the original version of the code. Users should consult the Borre et al. text for further details on running the codebases.
A total of 12 SDR codebases are aimed at processing each of the GNSS signals (see TABLE 1). All code files for each SDR are organized in the same subdirectories, and most of them have the same filenames.
Table 1. All GNSS signals that can be processed by the SDR collection, organized by their corresponding satellite systems.
All SDRs are set to work with a default configuration. They are all run using an init.m script, which collects user settings (input data file path, sampling frequency and so on) from the initSettings.m configuration script. Given this, the first file that users may want to modify is initSettings.m, to define the run settings for a given test. Most of the SDRs operate in an identical way, however some include particular features oriented at exploiting certain characteristics of the corresponding GNSS signal. The GPS L2C SDR, for example, gives the user the option of whether to process the pilot component of the signal.
The test samples available in the public directory were obtained in accordance with the characteristics depicted in TABLE 2 for every signal. The first two columns from the left show all the signals the SDR collection can process and the central carrier frequency at which they are transmitted. The third column gives the bandwidth selected in the recording process for every signal. This value must match the sampling frequency defined in the initSettings.m file for each SDR. Only three frequency bandwidths can be used to record GNSS data, so as to make the configuration structure more homogeneous across different SDRs. They were selected to ensure similar characteristics for each signal in terms of performance, encompassing most of the signal power for each modulation, but also keeping the recorded GNSS data files within a reasonable size.
Table 2. Summary of the tested GNSS signals’ center frequencies and the selected bandwidth (BW) for their processing. The common IF for all signals is 20 MHz.
All the signals were mixed to a common intermediate frequency (IF) of 20 MHz in the recording process. Both the frequency bandwidth and the IF are fundamental to obtain the expected results from each SDR codebase. These are set in the settings file. The default configuration was validated in the testing campaign explained in later sections, and should only be modified to meet the user’s specific needs, being aware that some SDR performance characteristics may also be affected.
BASIC GNSS SDR STRUCTURE
While the general SDR receiver structure is similar across all codebases, each comes with adjustments and/or additions to adapt the code to the format of a specific signal. The general codebase structure can be summarized in four major modules:
signal acquisition
tracking stage
navigation data decoding
position, velocity and time (PVT) computation.
An important remark is that the SDR collection developed is designed to process files of limited duration. The code is designed to use enough data to provide a successful initial acquisition, and then use a single set of satellites for the remaining execution. In other words, there is no extra logic oriented at acquiring or reacquiring satellites after the first acquisition is achieved.
Signal Acquisition.The design of the acquisition scheme depends on the characteristics of the signal the SDR is aiming to process. There are numerous GNSS signal configurations for each constellation that follow different strategies concerning spreading codes, navigation data and secondary codes, which must be accounted for in the acquisition codebase.
All codebases follow a fast Fourier transform (FFT) accelerated serial search-acquisition approach to obtain estimates of the signal’s carrier frequency and code delay, where a number of signal replicas are generated iteratively, separated by a defined frequency interval in the frequency domain. All the frequency offsets are arranged in what are known as frequency bins. This frequency separation will be referred to as the frequency step. The latter is inversely proportional to the integration time and tells the maximum error allowed in the carrier frequency estimate, which is half the frequency step. Both the frequency step and the coherent integration time are parameters that have a strong effect in acquisition results, as will be seen below.
Each local replica is correlated with the input signal to obtain a code-phase estimate. The length of this correlation is the so-called coherent integration time. The maximum correlation measurement from all frequency bins is then divided by the second maximum found. This ratio is called the peak metric and is used in all SDRs to give a measure of the magnitude difference between the maximum obtained and the remaining correlation results. If the peak metric is not high enough, this implies that the maximum is close to other cross-correlation products and so could not correspond to the result obtained after correlating both input and local replica signals with the right code-phase alignment. When the peak metric surpasses the threshold defined in initSettings.m, the satellite is considered to be acquired.
It is worth noting that in all SDR implementations the local replica is constructed by concatenating a whole primary code and a block of zeros of the same length. This prevents navigation bit transitions from affecting the correlation results. For example, GPS L2C-CM SDR’s acquisition correlates 40 milliseconds of data with 20 milliseconds of pseudorandom noise (PRN) spreading code followed by 20 milliseconds of zeros (the zero padding technique).
Tracking Stage.The tracking stage is oriented at refining and keeping track of the code and carrier estimates provided by the acquisition stage as well as demodulating the navigation data. This is achieved using feedback loops organized in channels, which are typically referred to as tracking channels. There will be as many tracking channels as the number of satellites acquired. Each tracking channel makes adjustments to the corresponding local signal replica for the given satellite, so that it resembles the real received signal as much as possible. When the replica is sufficiently accurate, the tracking loop locks onto the signal, removes the carrier and spreading code components, and starts registering data bit transitions. The task of every tracking channel is to account for signal variations so that they can keep locked on the signal for as long as the satellite is available for use.
Tracking channels implement two feedback loops, the delay lock loop (DLL) and the Costas phase lock loop (PLL). The former is focused on the signals’ code phase while the latter on the carrier phase. These modules depend on two major parameters that determine the properties of the loop filter: the damping ratio and the noise bandwidth. On the one side, the damping ratio controls how fast the filter reaches the settling stage. On the other side, the noise bandwidth informs the amount of noise allowed in the filter.
While all SDRs follow similar tracking loop schemes, some signals, such as GPS L2C, need some adjustments to the parameters mentioned above so that they provide the expected results, as we point out later. Tracking results are stored in a Matlab .mat file, but also can be assessed in the plot the tracking stage generates after it finishes processing all the channels.
FIGURES 1a and 1b show an example of two different tracking results plots, each of which include seven figures. These show the in-phase/quadrature (I/Q prompts), the navigation data bits decoded, the changes in the raw/filtered Costas loop and DLL discriminators, and the early-prompt-late metrics. Note that the plots in Figure 1a suggest the navigation data bits were demodulated successfully. In contrast, in Figure 1b, data bits cannot be distinguished because the tracking stage failed to demodulate the navigation message.
Figure 1. (a) shows the plot generated for a successful tracking channel. In contrast, (b) illustrates the results obtained when the tracking loop in question did not lock appropriately to the signal and therefore was not able to demodulate navigation data. (Image: Authors)
Navigation Data Decoding.This stage extracts the navigation data required by the SDR codebase to compute PVT estimates from the results delivered by the tracking stage. The latter outputs I/Q prompt samples representing data bits, containing the encoded navigation data. The navigation data format for each signal can be found in the interface control document issued by each satellite constellation operator.
The general process that each SDR implements to demodulate navigation data from I/Q samples is summarized as follows:
detect a preamble within data bits
arrange the bit sequence in the corresponding structures, such as frames
remove secondary code if present
de-interleave and decode
check if the bit stream has errors
extract navigation parameters
Once navigation parameters are extracted, they are stored and later used by the functions involved in the PVT computation stage.
PVT Computation.The PVT stage takes the decoded navigation data, computes satellite positions, and solves the geometry problem, whose solution is the receiver’s location.
As with all the other stages, all SDRs follow the same approach, and use the least-squares method to solve for a position estimate once all the data is available. Position estimates are delivered in both Earth-centered Earth-fixed and east-north-up coordinates.
Similarly to the tracking stage, the PVT computation stage returns a plot showing some PVT statistics to help the user get an idea of the PVT performance of the test conducted. FIGURES 2aand 2b show an example of two positioning plots obtained for two different data files.
Figure 2. (a) shows the plot of a priori, good statistics for the navigation solution; (b) shows a navigation plot for a file that presented a problem affecting the PVT solution. (Image: Authors)
EXPERIMENTAL SET-UP AND TESTING
In this section, we present the equipment we used in our tests (see FIGURE 3) and detail the process we followed to collect GNSS data, as well as the testing framework designed to exercise the SDR collection.
Figure 3: The antenna was connected to the RF port of the USRP. The USRP sampled the analog data delivered by the antenna using the TCXO as the reference oscillator. The resulting sampled data was stored in a Linux-based computer. (Image: Authors)
RF Antenna.The device used to sense the RF GNSS signals was a Trimble Zephyr2 antenna, which has enhanced capabilities for multipath minimization as well as low-elevation-angle satellite tracking properties.
The antenna was installed on the rooftop of the Ann and H.J. Smead Department of Aerospace Engineering Sciences building at the University of Colorado Boulder.
USRP and TCXO Devices.An Ettus Universal Software Radio Peripheral (USRP) B200 hardware SDR connected to an IQD temperature-compensated crystal oscillator (TCXO) was used to collect digital samples from GNSS analogue signals sensed by the antenna.
The B200 device was controlled by means of the USRP hardware driver (UHD) through a computer running a Linux operating system. UHD is a software application programming interface (API) that enables the development of code to manage USRP settings and operation.
PC Setup.The PC setup consisted of a Linux computerwith all the required drivers and program dependencies, as well as with Mathworks’ Matlab software installed. Matlab was used to program and automate the data recording process.
Recording Process.The equipment described in previous subsections was used to record data suitable for each SDR codebase. The process to obtain signal data for all 12 codebases was reduced to eight stages by selecting an adequate frequency bandwidth, as some signals share the same central carrier frequency (see Table 2).
For each stage, a total of 100 files with 61 seconds of I/Q GNSS data were recorded over a 24-hour time period. The I/Q samples recorded by the USRP were formatted as 8 bit sine carriers. All the data sets recorded are available together with a description file based on the Institute of Navigation’s metadata standard for GNSS.
TESTING FRAMEWORK
The workflow we followed to test every codebase from the collection is outlined in the following steps:
Record data samples.A set of one hundred files were recorded with 61 seconds of GNSS data.
Debug the SDR with the selected files. A debugging stage preceded every test case to ensure the codebase performed well enough, or else to make the required adjustments.
Run the SDR for one hundred trials. A total of one hundred tests, one per file, were performed for all SDRs.
Log metrics and present results.The results from all SDR stages (acquisition, tracking and data demodulation) were stored for each file. Also, each iteration returned a message that summarized the execution results.
All of the messages returned for every file corresponded to one of the cases summarized as follows:
Codebase issue. Message type returned when the codebase failed because of a coding issue.
No navigation solution. The codebase was not able to deliver a navigation solution either due to a malfunction of the codebase or due to a lack of satellite availability. Navigation solutions are only available when both the tracking channel and the navigation data demodulation stages are successful for more than three satellites.
Navigation solution with accuracy worse than 30 meters. A position solution fix more than 30 meters (in three dimensions) from the known antenna location was considered a non-accurate estimate.
Navigation solution with accuracy under 30 meters.When the 3D positioning error was < 30 meters, the navigation solution for the position was considered accurate.
All codebases passed a debugging stage before being tried with the whole set of available data. This was done to ensure that they performed as expected, and were able to achieve the required performance in terms of the metrics mentioned above in this section. An example of this debugging stage will be explained in further detail below. We take the GPS L2C codebase as an example of how all implementations were assessed in an attempt to improve their initial performance and make them more robust to code errors. See our proceedings paper for further details of our test cases.
GPS L2C Test Case.The problem observed for GPS L2C was that some satellites acquired with a high acquisition metric were failing the tracking stage. The result was that no navigation data was demodulated from them. An in-depth study was required to find out the adjustments needed in the codebase that would help to solve this issue.
The GPS L2C signal encompasses two signal components called civil moderate (CM) and civil long (CL). The CM component is formed by a spreading code that modulates a navigation message. The CL component is a pilot (data-less) signal modulated with a longer spreading code allowing for longer coherent and non-coherent integration times, yielding better sensitivity.
For CM signal acquisition, the 20 millisecond code length limits the coherent integration time to 20 milliseconds, due to the overlaid navigation message. This integration time defines the minimum frequency resolution required to obtain the expected correlation results. The CL component is used in the SDR to accumulate consecutive correlation results non-coherently, contributing to the receiver’s sensitivity by allowing it to operate with higher acquisition metrics in general.
The initial configuration for this SDR codebase is represented in TABLE 3a.
Table 3. Configurations for GPS L2C test case.
With this configuration, a total of 10 satellites were acquired. However, it was observed that for some satellites acquired with high peak metrics, it was not possible to demodulate their navigation data, and thus they were not considered for the navigation solution in later stages. This situation was abnormal, as typically this behavior is more characteristic of weaker signals whose bit transitions are too noisy to be decoded. This problem suggested that either the code-phase or the carrier frequency estimates (or both) were not accurate enough for each tracking channel to generate a proper replica to lock onto the input signal.
The first step taken to address this matter was to inspect the SDR’s acquisition stage for a file presenting the mentioned problems. For instance, taking a closer look at the carrier and code-phase 3D representation for those satellites acquired with a high acquisition metric that were not successfully tracked afterwards. After doing so, some satellites were identified with the irregular characteristics described above, as for example the PRN 10 satellite. PRN 10 is taken as a reference throughout this subsection.
The metric analyzed for PRN 10 was the matrix built by the acquisition’s serial search process. This matrix contains the correlation results obtained for each frequency bin. The width of each frequency bin is determined by the frequency step size defined in the configuration file. In this way, the smaller the frequency step, the more frequency bins that the corresponding matrix contains. This implies a better frequency resolution.
With this in mind, the frequency resolution was progressively increased by decreasing the frequency step size. Extra logic had to be added to the acquisition algorithm to implement this feature. It was found that when using a step size of 6.5 Hz, the tracking stage was then able to lock and demodulate navigation bits from PRN 10 effectively. This was the most significant determining factor to overcome the issue in question for the majority of the satellites available. However, other smaller adjustments also improved tracking results in general. These are depicted in TABLE 3b.
CODE AVAILABILITY
All the resources concerning the SDR collection are publicly available at the portal hosted by University of Colorado Boulder. Through this portal, all the GNSS codebases along with the data sets for testing can be acquired, as well as access to the discussion forum.
CONCLUSION
The first version of the SDR collection was made available after the seminal text by Borre et al. was published and consisted of a GPS L1C/A SDR and multiple data sets. From then on, this project kept evolving by adding more SDRs as new GNSS signals emerged across different satellite constellations.
Our most recent work was to collect all the SDR codebases, arrange them in a common format, and test each implementation to assert their robustness and extract statistics concerning their performance.
Future work will be dedicated to adding more features aiming at refining the PVT estimates delivered by each SDR.
More progress is expected to be made soon, with additional improvements made in the GNSS laboratory. In addition, there is plenty of room for contributions from other researchers who want to support and collaborate with this open-source initiative. Our portal provides a convenient way to manage these contributions.
ACKNOWLEDGMENTS
We thank the many individuals who collaborated in the development of the open-source GNSS SDR collection.
This article is based on the paper “A Collection of SDRs for Global Navigation Satellite Systems (GNSS)” presented at ION ITM 2022, the 2022 International Technical Meeting of the Institute of Navigation, Jan. 25–27, 2022.
JOAN BERNABEU is a Ph.D. student at the Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse, France. He also works as a satellite navigation engineer for GMV, Spain.
NICOLAS GAULT is a Ph.D student at École Nationale d’Aviation Civile, Toulouse, France. He was a visiting scholar in the Department of Aerospace Engineering Sciences at the University of Colorado (CU) Boulder in 2020-2021.
YAFENG LI is an associate professor in the School of Automation at the Beijing Information Science and Technology University, China. He was a visiting researcher with the Department of Aerospace Engineering Sciences, CU Boulder in 2017–18.
DENNIS M. AKOS is a faculty member in the Department of Aerospace Engineering Sciences at CU Boulder.
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.
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First GNSS-SDR Galileo standalone position fix using the four available satellites (Position obtained at the CTTC headquarters on 2013-Nov-10 15:52:14 UTC).
For the first time, position fixes in real time using signals from Galileo have been achieved with an open source software receiver. The milestone was achieved by a research team from the Statistical Inference Department at the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), which manages the development of the open source project GNSS-SDR.
Professional, full-featured receivers are expensive, and even in those cases the users have limited access (if any) to know exactly how position and time information were computed, CTTC said. In addition, these receivers exhibit very few upgrading capabilities. A software receiver allows all kind of modifications and inspections. “GNSS-SDR unleashes the full potential of the signals and, best of all, it is open and for free,” said Carles Fernández-Prades, GNSS-SDR project manager and Head of the Communications Systems Division at CTTC.
GNSS-SDR 2D ENU coordinates precision for the Galileo position fix.
A GNSS software receiver is a computer program that performs all the signal processing from raw satellite signals to the computation of position, velocity and time, just as is done by the GPS chips that are embedded in smartphones and other devices with satellite-based positioning capabilities. The key difference relies on the great flexibility in the design, upgradability and the experimentation possibilities that the software version allows, in opposition to integrated circuits, true black boxes with inputs and outputs but with no accessible information about what is going on inside of them.
“With GNSS-SDR, researchers and technology enthusiasts can easily change the implementation of a certain functional block and assess the impact of that change on the whole receiver performance,” said Pau Closas, GNSS-SDR scientific advisor and Head of the Statistical Inference Department at CTTC. “This paves the way to innovative mass-market, industrial and scientific applications that could make use of Galileo signals but require non-standard features which are not present in mass-market receivers nor in costly professional equipment.”
The first Galileo-based positioning fix, obtained by Javier Arribas using a general purpose GNSS antenna and a RF front-end connected to a commodity PC running GNSS-SDR represents an important milestone in the research on GNSS receiver design. “Next steps will be devoted to provide outputs in standard formats that will allow the application of geodesic-grade tools for extremely precise positioning (on the order of centimeters) and higher degrees of reliability,” Arribas said.
GNSS-SDR is the first open source solution that offers this possibility, CTTC said. The source code released under the GNU General Public License (GPL) secures practical usability, inspection, and continuous improvement by the research community, allowing the discussion based on tangible code and the analysis of results obtained with real signals. The source code is complemented by a development ecosystem, consisting of a website, as well as a revision control system, instructions for users and developers, and communication tools.
With GNSS-SDR, researchers from CTTC (with the aid of an open community created around the project, such as the students participating in the Google Summer of Code program in 2012 and 2013 Luis Esteve, Mara Branzanti, Daniel Fehr and Marc Molina) are offering a tool that fosters the use of GPS and Galileo signals in unexpected new ways, making possible applications with unforeseen benefits in a wide range of fields, such as geodesy, robotics, unmanned vehicles and safety-related systems.
