Global Navigation Satellite System Signal Processing Research Articles

Dual-assisted high-precision tracking technique for wideband multiplexed signals in new generation GNSS

With the evolution of Global Navigation Satellite System (GNSS), new generation GNSS signals have adopted the dual-frequency multiplexing modulation techniques, which jointly modulate multiple signals located on multiple sub-frequencies into a Wideband Multiplexed Signal (WMS). Although WMSs were proposed initially to reduce the complexity of satellite transmitters and improve the transmission efficiency of signals, their multi-component structures and wide root mean square bandwidths introduced by high-frequency subcarriers also provide the possibility to improve the GNSS ranging precision. Therefore, this paper proposes a Dual-assisted Multi-component Tracking (DMT) technique, which can not only fully use high-frequency subcarriers in WMSs, but also effectively track carrier, subcarrier, and code by jointly utilizing all components in WMS. In this paper, the tracking and ranging performances of DMT are comprehensively analyzed theoretically and by simulation and real experiments. The results show that compared with existing WMS tracking methods, DMT can achieve tracking results with lower tracking jitters and ranging results with higher precision, providing a highly advantageous solution for new generation GNSS signal processing.

Implementation of a High-Sensitivity Global Navigation Satellite System Receiver on a System-on-Chip Field-Programmable Gate Array Platform.

This paper presents the design, proof-of-concept implementation, and preliminary performance assessment of an affordable real-time High-Sensitivity (HS) Global Navigation Satellite System (GNSS) receiver. Specifically tailored to capture and track weak Galileo E1b/c signals, this receiver aims to support research endeavors focused on advancing GNSS signal processing algorithms, particularly in scenarios characterized by pronounced signal attenuation. Leveraging System-on-Chip Field-Programmable Gate Array (SoC-FPGA) technology, this design merges the adaptability of Software Defined Radio (SDR) concepts with the the robust hardware processing capabilities of FPGAs. This innovative approach enhances power efficiency compared to conventional designs relying on general-purpose processors, thereby facilitating the development of embedded software-defined receivers. Within this architecture, we implemented a modular GNSS baseband processing engine, offering a versatile platform for the integration of novel algorithms. The proposed receiver undergoes testing with live signals, showcasing its capability to process GNSS signals even in challenging scenarios with a carrier-to-noise density ratio (C/N0) as low as 20 dB-Hz, while delivering navigation solutions. This work contributes to the advancement of low-cost, high-sensitivity GNSS receivers, providing a valuable tool for researchers engaged in the development, testing, and validation of experimental GNSS signal processing techniques.

Low-latency GNSS multipath simulation and building wall detection in urban environments

Precise navigation for fully autonomous driving—especially in dense urban areas—requires periodic precise position estimates. Global Navigation Satellite System (GNSS) technology has the potential to provide absolute positioning accuracy at a centimeter level. However, buildings in urban environments cause signal distortions and signal reflections—the so-called multipath—which are the most challenging parts in the GNSS error budget. Hence, we developed a scalable real-time multipath simulator for mitigating potential multipath receptions. The simulator uses three-dimensional (3D) building information, satellite, and user positions. The key drivers of latency are the calculation of reflection, diffraction, and line-of-sight, as well as the response time of the 3D building model database. The memory manager of the graphic processing units (GPUs) in combination with a dedicated load balancer enables fast and efficient multipath analysis. Selected case studies demonstrate the simulator’s potential to significantly improve the position accuracy of the processing engine. The use of the multipath simulator reduces the error in 61% of the error measurements in a stress test scenario to less than half of the non-multipath processing. The scalability of the simulator is demonstrated by combining the multipath simulator with a traffic simulator. Furthermore, we present a novel methodology for the detection of walls using GNSS signals to better account for incomplete or erroneous 3D building information in GNSS signal processing.

Non-Binary PRN-Chirp Modulation: A GNSS Fast Acquisition Signal Waveform

In this letter, we propose a new non-binary modulation which allows both Global Navigation Satellite Systems (GNSS) synchronization and the demodulation of non-binary symbols, without the need of a pilot signal, with the aim to provide a fast first position, velocity and time fix. The waveform is constructed as the product of i) a pseudo-random noise sequence with good auto-correlation and cross-correlation properties, and ii) a chirp spread spectrum family, which allows to demodulate non-binary symbols even if the signal phase is unknown. In order to demodulate the data, a bank of non-coherent matched filters is proposed. Because of the particular modulation structure, the receiver is capable to demodulate the navigation message faster while allowing the basic GNSS signal processing functionalities. Illustrative results are provided to support the discussion.

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning.

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

Probabilistic approach to detect and correct GNSS NLOS signals using an augmented state vector in the extended Kalman filter

Non-line-of-sight (NLOS) global navigation satellite system (GNSS) signals are a major factor that limits the GNSS positioning accuracy in urban areas. An advanced GNSS signal processing technique, the vector tracking loop (VTL), has been applied to NLOS detection and correction, and its feasibility and superior performance have been reported in recent studies. In a VTL-based GNSS receiver, the navigation solutions (i.e., position, velocity and time (PVT)) are used to predict the signal tracking loop parameters. The difference between the predicted signal and the received signal within the code discriminator output can be used to detect NLOS reception. We generate the probability of NLOS detection by modeling the code discriminator outputs using Gaussian fitting. If this probability is larger than a predefined threshold, NLOS reception is deemed to occur. Then, the NLOS-induced pseudorange measurement bias is estimated as a state variable in the state vector, i.e., an augmented state vector is created for the extended Kalman filter. Two GPS L1 C/A signal datasets from a static test and a dynamic test are investigated using the proposed algorithm. The experimental results indicate that when NLOS reception is present, the proposed approach outperforms the other two methods, i.e., the standard VTL method without considering NLOS reception and the VTL-based NLOS detection and correction method with multicorrelators, in terms of the positioning performance. In addition, the proposed approach has a lower computational load than the VTL method with multicorrelators.

Digital Filtering of Railway Track Coordinates in Mobile Multi-Receiver GNSS Measurements.

The article discusses an important issue in connection with the technique of mobile Global Navigation Satellite System (GNSS) measurements of railway track coordinates, which is digital filtering performed to precisely determine railway track axes. For this purpose, a measuring technique is proposed which bases on the use of a measuring platform with a number of appropriately distributed GNSS receivers, where two of them determine the directional base vector of the platform. The receivers used in the research had high measuring frequency in the Real Time Kinematic (RTK) operating mode and enabled correction of the obtained results in post–processing. A key problem discussed in the article is the method for assessing the quality of the measurement results obtained from GNSS receivers, and their preparation for further processing making use of geometrically constrained parameters of the base vector and specialized digital filtering, among other elements, to precisely determining the track axis. The obtained results confirm the applicability of the used method of GNSS signal processing.

High Precision Pseudo-Range Measurement in GNSS Anti-Jamming Antenna Array Processing

Radio frequency interference has become a rising problem to the signal of the Global Navigation Satellite System (GNSS). An effective way to achieve anti-jamming is by using an antenna array in GNSS signal processing. However, antenna array processing will cause a decline in the accuracy of pseudo-range measurements because of the channel mismatch and some other non-ideal factors. To solve this problem, space–time or space–frequency adaptive array processing is widely used for interference cancellation while constraining the delay of each antenna at the same time. In this paper, an anti-jamming algorithm with a time-delay constraint is proposed, where one antenna is chosen as the reference and data from other antennas is corrected based on the signal received from it. The deduction and simulation results show that the proposed algorithm can effectively improve the accuracy of pseudo-range measurements without degradation of anti-jamming performance.

Software-Defined Radio GNSS Instrumentation for Spoofing Mitigation: A Review and a Case Study

Recently, several global navigation satellite systems (GNSS) emerged following the transformative technology impact of the first GNSS: US Global Positioning System (GPS). The power level of GNSS signals as measured at the earths surface is below the noise floor and is consequently vulnerable against interference. Spoofers are smart GNSS-like interferers, which mislead the receivers into generating false position and time information. While many spoofing mitigation techniques exist, spoofers are continually evolving, producing a cycle of new spoofing attacks and counter-measures against them. Thus, upgradability of receivers becomes an important advantage for maintaining their immunity against spoofing. Software-defined radio (SDR) implementations of a GPS receiver address such flexibility but are challenged by demanding computational requirements of both GNSS signal processing and spoofing mitigation. Therefore, this paper reviews reported SDRs in the context of instrumentation capabilities for both conventional and spoofing mitigation modes. This separation is necessitated by significantly increased computational loads when in spoofing domain. This is demonstrated by a case study budget analysis.

Robust transform domain signal processing for GNSS

Robust signal processing techniques are effective mitigation approaches that can enable Global Navigation Satellite System (GNSS) receiver operations even in the presence of strong interference. We extend robust GNSS signal processing to the case where interference admits a sparse representation in a transform domain (TD). More specifically, an orthonormal transformation is used to project the received GNSS samples into an appropriate TD. Robust signal processing strategies are then applied. The robust transform domain (RTD) cross-ambiguity function (CAF) is obtained by applying a zero-memory nonlinearity (ZMNL) to the TD samples, which, in turn, are used for the evaluation of the CAF. Different nonlinearities are analyzed and different implementations for the evaluation of the RTD CAF are proposed. The RTD approach is characterized from a theoretical point of view, with the usage of Monte Carlo simulations and through the processing of GNSS signals generated with a hardware simulator.

Continuous Reproducibility in GNSS Signal Processing

This paper discusses the reproducibility of scientific experiments in which global navigation satellite system (GNSS) signals play a role. After analyzing the factors that impact the reproducibility of an experiment in the given context, this paper proposes a methodology that, leveraging on software containerization technologies and the best practices from professional software development, ensures the automated reproduction of scientific experiments involving GNSS data and software-defined GNSS receivers, including the generation of figures or tables of a research publication, while fostering scientific collaboration and contributing to mitigate the effects of software aging in mutating computer environments. In order to show a practical implementation of the proposed work flow, the authors propose a simple experiment based on a freely available open source software-defined receiver and the automation of its execution in a popular online platform.

GNSS receiver implementations to mitigate the effects of commensurate sampling frequencies on DLL code tracking

The sampling frequency of a digitized intermediate frequency signal has a strong effect on the measurement accuracy of Global Navigation Satellite System (GNSS) receivers. The delay-locked loop tracking error is significant when the sampling frequency is an integer multiple of the code chipping rate, the so-called commensurate sampling frequency, and the number of distinct instantaneous residual code phases is low. This results in distortions of the correlation shape and discriminator functions that lead to a significant accuracy degradation. These effects are most pronounced when the sampling frequency is low. Notwithstanding, it is generally good for receivers to keep the sampling frequency to a minimum owing to the processing load and power consumption. It creates a challenge for existing GNSS signal processing techniques. Random, sine and sawtooth jitters have been found to mitigate these distortions considerably. A software algorithm and two hardware receiver implementations of these solutions are proposed. A register-based architecture can be directly applied to the conventional receiver architecture, while the increase in resource and power consumption is insignificant. A RAM-based design cannot only considerably minimize utilized resources but also slightly reduce the power consumption compared to the conventional architecture.

High Sensitivity and Fast Acquisition Signal Processing Techniques for GNSS Receivers: From fundamentals to state-of-the-art GNSS acquisition technologies

Higher sensitivity and faster acquisition can be two conflicting goals for a global navigation satellite system (GNSS) acquisition function, and both of the goals must be considered in the development of GNSS signal processing techniques to meet the demands for location-based services (LBSs) in GNSS-challenged environments. This article introduces the fundamentals of GNSS acquisition functions and various GNSS acquisition techniques for new GNSS signals and investigates recent acquisition techniques achieving high sensitivity and fast acquisition. It provides useful information for engineers who study state-of-the-art GNSS signal acquisition techniques and want to understand the challenges involved in improving GNSS acquisition sensitivity and acquisition time.

Myriad non‐linearity for GNSS robust signal processing

The robustness of standard correlation-based Global Navigation Satellite System (GNSS) signal processing can be significantly improved by pre-processing the input samples with a zero-memory non-linearity (ZMNL). A paradigm for the design of ZMNLs is provided by the M-estimator framework where heavy-tailed probability density functions (pdfs) are used to model the impairments affecting the input samples. The myriad non-linearity, obtained considering a Cauchy pdf, is analysed for the acquisition and tracking of GNSS signals in the presence of pulsed interference. The impact of the myriad non-linearity is theoretically characterised and Monte Carlo simulations are used to support theoretical findings. Finally, real GNSS signals collected in the presence of jamming are processed: the myriad non-linearity provides a significant performance improvement with respect to standard GNSS signal processing which is unable to acquire and track the samples affected by interference.

Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction in challenging weather conditions

The Global Navigation Satellite Systems (GNSS) are one of the very few tools that can provide continuous, unbiased, precise and robust atmosphere condition information. The extensive research of GNSS space-based segment (e.g. available precise, real-time satellite orbits and clocks), unlimited access to the ground-based Continuously Operating Reference Stations (CORS) GNSS networks along with the well established data processing methods provides an unprecedented opportunity to study the environmental impacts on the GNSS signal propagation. GNSS measurements have been successfully used in precise positioning, tectonic plate monitoring, ionosphere studies and troposphere monitoring. However all GNSS signals recorded on the ground by CORS are subject to ionosphere delay, troposphere delay, multipath and signal strength loss. Nowadays, the GNSS signal delays are gradually incorporated into the numerical weather prediction (NWP) models. Usually the Zenith Total Delay (ZTD) or Integrated Water Vapour (IWV) have been considered as an important source of water vapour contents and assimilated into the NWP models. However, successful assimilation of these products requires strict accuracy assessment, especially in the challenging severe weather conditions.In this study a number of GNSS signal processing strategies have been verified to obtain the best possible estimates of troposphere delays using a selection of International GNSS Service (IGS) orbit and clock products. Three different severe weather events (severe storm, flash flooding, flooding) have been investigated in this paper. The strategies considered are; 1) Double Differenced (DD) network solution with shortest baselines, 2) DD network solution with longest baselines, 3) DD baseline-by-baseline solution (tested but not considered), 4) Zero Differenced (ZD) Precise Point Positioning (PPP) based on ambiguity float solutions, all with precise orbits and clocks, and real time clocks and predicted orbits. The quality of the estimates obtained has been evaluated against radiosonde measurements, Automatic Weather Station (AWS) observations, NWP (assimilation step without ground-based GNSS data) and ZTD estimates from the well established IGS processing centre, the Center of Orbit Determination in Europe (CODE). It shows that the ZTD and IWV estimates from the DD short baseline solution are robust with usually a very small bias (−2.7 to −0.8mm) and errors of less than 10mm (7.6–8.5) (ZTD) or 3mm (2.6–2.7) (IWV). The DD short baseline network solution was found to be the most reliable method in the considered case studies, regardless of the type of orbits and clocks applied.

Gnss Signal Processing in Gpu

ABSTRACT Signal processing of the global navigation satellite systems (GNSS) is a computationally demanding task due to the wide bandwidth of the signals and their complicated modulation schemes. The classical GNSS receivers therefore utilize tailored digital signal processors (DSP) not being flexible in nature. Fortunately, the up-to-date parallel processors or graphical processing units (GPUs) dispose sufficient computational power for processing of not only relatively narrow band GPS L1 C/A signal but also the modernized GPS, GLONASS, Galileo and COMPASS signals. The performance improvement of the modern processors is based on the constantly increasing number of cores. This trend is evident not only from the development of the central processing units (CPUs), but also from the development of GPUs that are nowadays equipped with up to several hundreds of cores optimized for video signals. GPUs include special vector instructions that support implementation of massive parallelism. The new GPUs, named as general-purpose computation on graphics processing units (GPGPU), are able to process both graphic and general data, thus making the GNSS signal processing possible. Application programming interfaces (APIs) supporting GPU parallel processing have been developed and standardized. The most general one, Open Computing Language (Open CL), is now supported by most of the GPU vendors. Next, Compute Unified Device Architecture (CUDA) language was developed for NVidia graphic cards. The CUDA language features optimized signal processing libraries including efficient implementation of the fast Fourier transform (FFT). In this paper, we study the applicability of the GPU approach in GNSS signal acquisition. Two common parallel DSP methods, parallel code space search (PCSS) and double-block zero padding (DBZP), have been investigated. Implementations in the C language for CPU and the CUDA language for GPU are discussed and compared with respect to the acquisition time. It is shown that for signals with long ranging codes (with 10230 number of chips - Galileo E5, GPS L5 etc.). Paper presented at the "European Navigation Conference 2012", held in Gdansk, Poland

NUSAR - a software platform to process GPS/Galileo OS signals

The foreseen developments in Global Navigation Satellite Systems (GNSS) bring forth new opportunities in GNSS signal processing to cope with the new modulations. The NUSAR platform is a software implementation of signal acquisition and tracking in the Open Service (OS) E1-L1 band (GPS and Galileo), providing real-time observables such as code/ carrier phase, Doppler frequency, and C/N0. The platform consists of an antenna, a wideband front-end, and a laptop running the NUSAR software. The raw measurements obtained from the GNSS satellites can then be used for different navigation and control applications, as the software implementation allows for efficient tailoring of the signal processing algorithms to specific needs. This work presents some of the early results obtained with the NUSAR platform when processing Galileo's GIOVE-B signal, modulated with CBOC.

Acousto-optic devices using lead molybdate and related compounds: Bell Telephone Labs Inc US patent 3617931 (1971) filed 5 May 1969

The Global Navigation Satellite Systems (GNSS) are one of the very few tools that can provide continuous, unbiased, precise and robust atmosphere condition information. The extensive research of GNSS space-based segment (e.g. available precise, real-time satellite orbits and clocks), unlimited access to the ground-based Continuously Operating Reference Stations (CORS) GNSS networks along with the well established data processing methods provides an unprecedented opportunity to study the environmental impacts on the GNSS signal propagation. GNSS measurements have been successfully used in precise positioning, tectonic plate monitoring, ionosphere studies and troposphere monitoring. However all GNSS signals recorded on the ground by CORS are subject to ionosphere delay, troposphere delay, multipath and signal strength loss. Nowadays, the GNSS signal delays are gradually incorporated into the numerical weather prediction (NWP) models. Usually the Zenith Total Delay (ZTD) or Integrated Water Vapour (IWV) have been considered as an important source of water vapour contents and assimilated into the NWP models. However, successful assimilation of these products requires strict accuracy assessment, especially in the challenging severe weather conditions.In this study a number of GNSS signal processing strategies have been verified to obtain the best possible estimates of troposphere delays using a selection of International GNSS Service (IGS) orbit and clock products. Three different severe weather events (severe storm, flash flooding, flooding) have been investigated in this paper. The strategies considered are; 1) Double Differenced (DD) network solution with shortest baselines, 2) DD network solution with longest baselines, 3) DD baseline-by-baseline solution (tested but not considered), 4) Zero Differenced (ZD) Precise Point Positioning (PPP) based on ambiguity float solutions, all with precise orbits and clocks, and real time clocks and predicted orbits. The quality of the estimates obtained has been evaluated against radiosonde measurements, Automatic Weather Station (AWS) observations, NWP (assimilation step without ground-based GNSS data) and ZTD estimates from the well established IGS processing centre, the Center of Orbit Determination in Europe (CODE). It shows that the ZTD and IWV estimates from the DD short baseline solution are robust with usually a very small bias (− 2.7 to − 0.8 mm) and errors of less than 10 mm (7.6–8.5) (ZTD) or 3 mm (2.6–2.7) (IWV). The DD short baseline network solution was found to be the most reliable method in the considered case studies, regardless of the type of orbits and clocks applied.