Multimodal Location Spoofing: Federated Detection With RAIM-Based Self-Labeling

The combination of multiple constellations of the Global Navigation Satellite System (GNSS) provides a great possibility to improve the integrity of the satellite navigation. However, with the increase of the number of satellites, the probability of multiple outliers increases. Although a number of studies analyzed the performance of the combined GNSS Receiver Autonomous Integrity Monitoring (RAIM), the analysis of the case of multiple outliers is still lacking. In this paper, a Maximum Undetectable Position Bias (MUPB) scheme based on Bias Integrity Threat (BIT), is used to analyze the horizontal availability of RAIM with multiple outliers. The theoretical background for BIT and MUPB has been analyzed. Next, detailed simulations and analyses with single or combined GPS/BDS (BeiDou Satellite Navigation System)/GLONASS constellations were conducted to evaluate the performance of RAIM. In the simulated scenarios with 70 satellites (GPS 32+BDS 14+GLONASS 24) and 2 or 3 outliers occurring simultaneously, this paper compared the MUPB and the Horizontal Alert Level (HAL) of different aviation approach phases and analyzed the results.

GNSS (“Global Navigation Satellite Systems”) applications with high security requirements usually manage integrity with Receiver Autonomous Integrity Monitoring (RAIM). Developed originally for aeronautics, RAIM techniques perform a consistency check on satellite measurements to provide a reliable positioning to the user. Modern versions of RAIM provide high performances for aviation. However, the problematics for terrestrial navigation, especially in urban environments, are quite different and have motivated the development of a new integrity process derived from the RAIM concept. In this paper, we introduce a new detection algorithm based on the a priori coherence between the direct received raw signals and the estimated position provided by the navigator. The use of raw satellite signals allows the detection of more estimation inconsistencies. The major differences between the proposed approach (a priori) and the RAIM approach (a posteriori) are outlined from a theoretical standpoint and illustrated by simulated data. Furthermore, we introduce a new criterion derived from both processes which proves their complementarity. Hence, this paper demonstrates the relevance of such a joint assessment in urban scenarios with degraded signal propagation conditions.

Global Navigation Satellite Systems (GNSSs) have both internal and independent methods to maintain integrity. This chapter discusses two different methods– GNSS‐ receiver autonomous integrity monitoring and use of ground monitoring stations to monitor the health of the satellites, as is done via space‐based augmentation system (SBAS) and the ground‐based augmentation system. SBAS integrity is based on the premise that errors not detected or corrected in the operational environment can become threats to integrity and, if not mitigated, can become hazards to the user. The user must be protected from residual errors that can become threats to the integrity data that could result in hazardously misleading information being transmitted to the user. The SBAS system must compute estimates of the reference receiver clocks and GNSS/geostationary Earth orbit satellite clock errors. The chapter also describes some approaches that may be used to eliminate and minimize data errors, mitigate integrity threats, and satisfy the safety integrity requirements.

A global navigation satellite system (GNSS)-based navigation is a challenging task in a signal-degraded environments where GNSS signals are distorted by multipath and attenuated by fading effects: the navigation solution may be inaccurate or unavailable. A possible approach to improve accuracy and availability is the joint use of measurements from different GNSSs and quality check algorithms; this approach is investigated here using live GPS and Galileo signals. A modified receiver autonomous integrity monitoring (RAIM) algorithm, including geometry and separability checks, is proposed to detect and exclude erroneous measurements: the multi-constellation approach provides redundant measurements, and RAIM exploits them to exclude distorted observations. The synergy between combined GPS/Galileo navigation and RAIM is analyzed using live data; the performance is compared to the accuracy and availability of a GPS-only solution. The tests performed demonstrate that the methods developed are effective techniques for GNSS-based navigation in signal-degraded environments. The joint use of the multi-constellation approach and of modified RAIM algorithms improves the performance of the navigation system in terms of both accuracy and availability.

In order to foster the development of Global Navigation Satellite Systems (GNSS) for land navigation services, there is a pressing need for providing a trust level of the localization solution, especially for liability critical applications. In view of such need, integrity monitoring aims to compute protection levels that successfully bounds positioning errors in nominal conditions. Conventional Receiver Autonomous Integrity Monitoring (RAIM) algorithms have been widely used for integrity monitoring especially for aircraft navigation. Conventional RAIM starts out by supposing a Gaussian measurement errors model with known means and variances. However, this assumption does not hold in dense urban environment. Therefore, we propose novel algorithms that dispenses with this classical assumption. We use external information provided by a 3D GNSS simulation to characterize GNSS errors in urban areas. We propose two approaches for protection levels computation based on comparison between predicted 3D pseudo-ranges bias and computed thresholds. These approaches give a new vision on integrity control based on indicators on the pseudo-ranges bias. Experimental results show that proposed algorithms give an acceptable success rate on integrity monitoring in a harsh areas.

Robust GPS-INS Outlier Accommodation: A Soft-thresholded Optimal Estimator

Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal weakness, GNSS is susceptible to interference, fading, and jamming, which reduces the position accuracy. Therefore, it would be beneficial to have a simple and highly accurate model for detecting the jamming signals to improve the GNSS receiver accuracy. In this paper, we propose a hybrid deep learning (DL) model for predicting jamming signals. Initially, we utilize a feature selection algorithm that combines mutual information (MI) with the minimal redundancy maximum relevance (mRMR) technique to identify the most crucial features. Subsequently, the model undergoes training using a soft attention-double-layer bidirectional long short-term memory (A-DBiLSTM) model. This particular model has shown outstanding performance in comparison to other DL models when applied to datasets collected from both kinematic and static jamming scenarios. To assess the effectiveness and efficiency of the proposed MI feature selection algorithm, we evaluate its performance through the calculation of confusion matrices and conducting numerical simulations. The simulation results of the A-DBiLSTM model demonstrate higher accuracy, precision, recall, and F1Score\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {F1_{Score}}$$\\end{document} of 98.82%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.82\\%$$\\end{document}, 98.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.4\\%$$\\end{document}, 98.68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.68\\%$$\\end{document}, and 98.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.36\\%$$\\end{document}, respectively. By employing the MI feature selection algorithm, dimensionality reduction is achieved. Moreover, the MI feature selection algorithm reduces 19%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$19\\%$$\\end{document} of the learning time with almost the same accuracy.

This paper evaluates the potential capacity of Receiver Autonomous Integrity Monitoring (RAIM) algorithms for Global Positioning System (GPS) and GPS Aided GEO Augmented Navigation (GAGAN) to support approach and landing phases of flight with vertical guidance. As the traditional RAIM does not support vertical guidance, Advanced RAIM (ARAIM) with potential to achieve worldwide coverage of vertical guidance, is used. These algorithms have focused on the Horizontal Protection Level (HPL) and Vertical Protection Level (VPL). User position is computed by detecting and excluding faults within the navigation solution and position accuracies are also provided. It is found that using RAIM, Position Dilution of Precision (PDOP) mean was good (1.5674) and Position Error (PE) was estimated to be 9.4042m with Fault Detection and Exclusion (FDE) implementation. While on the other hand, to increase horizontal and vertical accuracies, Advanced RAIM algorithm is implemented using GPS and GAGAN data. In case of GPS, Horizontal Protection Error (HPE) mean is 32.8541m and Vertical Protection Error (VPE) mean is 19.9901m. In case of GAGAN, HPE mean is 2.7190m and VPE mean is 3.3010m, thus achieving 95 percent accuracy requirement.

Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

Global Navigation Satellite Systems (GNSSs) are theoretically able to provide accurate, three-dimensional, and continuous positioning to an unlimited number of users. An important shortcoming of GNSS is the lack of integrity, defined as the ability of a system to provide timely warnings in case of malfunction; this problem is especially felt in safety-of-life applications such as aviation. A common way to fill this gap is the use of Receiver Autonomous Integrity Monitoring (RAIM) techniques, which are able to provide integrity information by analyzing redundant measurements. A possible RAIM functionality is the ability to identify, and so discard, anomalous measurements; this functionality has made RAIM very useful also in case of severe signal degradation, such as in urban or dense vegetation areas, where blunders are common. PANG-NAV is a tool, developed by the PArthenope Navigation Group, able to process GNSS measurements (from RINEX files) in order to obtain a position solution. The core of PANG-NAV is the single point positioning (SPP) technique, including a RAIM functionality. A multi-constellation solution, with GPS and Galileo, can be provided. Both static processing and kinematic processing are possible, and in cases where ground truth is available, error analysis can be carried out.

Integrity Monitoring (IM) for navigation systems is critical to ensure the safety of auto-driving vehicles. It refers to accurately raising alarms to a user when navigation positioning errors exceed the performance limitation. Although Receiver Autonomous Integrity Monitoring (RAIM) methods have been widely applied for Global Navigation Satellite System (GNSS) observations via a consistency checking algorithm, the integrity performance for auto-driving would suffer from a large degradation due to the low satellite visibility with signal occlusions and Non-Line-of-Sight (NLOS) receptions in urban environments. To alleviate this problem, the Fifth-generation (5G) communication signals are used as an auxiliary urban navigation source to increase the number of observations, and then an IM method for the integration of GNSS/5G (G5GIM) is proposed to improve the integrity performance. Specifically, the positioning reference signals of the 5G system in urban canyons are modeled to enrich the observations of the vehicle navigation, followed by the Horizontal Protection Level (HPL) estimation method and the fault detection algorithm for the integration of GNSS and 5G observations. Experimental results on the simulation and UrbanNav datasets show that the proposed G5GIM method significantly outperforms the existing GNSS RAIM method in terms of HPL reduction, fault detection rate and positioning accuracy improvement.

Global Navigation Satellite Systems (GNSS) have continually involved in our daily life in the past decades. Among all these GNSS-based applications, there is an increasing number of GNSS-based urban transport applications, especially the critical liability ones, such as the Electronic Toll Collection (ETC) and the Intelligent Transport System (ITS). For these GNSS-based applications, not only positioning accuracy but also its reliability is required. Latter has attracted more and more attention from urban GNSS users. Yet urban environments present great challenges for common commercial GNSS receivers since the existence of local effects such as multipath and the Non-Line-of-Sight (NLOS) receptions. The main objective of this paper is to investigate the integrity performance one can expect from a low-cost common GNSS receiver. Under this framework, this paper proposes an innovation-based integrity monitoring scheme by using an Extended Kalman Filter (EKF) with the help of carrier-power-to-noise ratio ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C/N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) weighting. The derivations of the Horizontal Protection Level (HPL) for the EKF innovation-based integrity monitoring is also presented in detail. The accuracy and integrity performances are evaluated with real GPS data collected in urban environments. Compared to the classic residual-based snapshot Receiver Autonomous Integrity Monitoring (RAIM), the innovation-based EKF integrity monitoring scheme presents more advantages in terms of accuracy and integrity. With the real dataset collected in urban canyon of Nantes center, the results show that the average size of horizontal position error has been reduced by 1.15 meter with the proposed approach compared to the classic weighted RAIM, the median size of HPL has been reduced by 7.5 meters and the algorithm availability is 100% instead of 98.57 %.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

It has been recognized that the risk from the vulnerability of GPS can lead to the extreme damage in the infrastructure of the civil and military in recent years. As an example, the intentional interference to GPS signal, named GPS jamming, was really performed to misguide GPS guided weapons during Iraq war in 2003, and the fact has also followed by the serious issues on GPS in civilian community. In the modernized military society, the navigation warfare(NAVWAR) based on the GPS jamming has been emerged and introduced as a military operation. The intentional interference to the future global navigation satellite system(GNSS) involving GPS must be also an important issue to civilian users in near future. This study is focused on the fundamental research prior to the research on "Potential principle of NAVWAR" under NAVWAR of the future warfare. In this paper, we would study on the investigation of NAVWAR based on electronic warfare(EW) and analyze characteristics of the jamming against GNSS's receivers. Then the general mechanism on GNSS jamming is proposed.

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.