PANG-NAV: a tool for processing GNSS measurements in SPP, including RAIM functionality

This study investigates the accuracy of offline Global Navigation Satellite System (GNSS) in remote areas where there are no internet connectivity. The research focused on offline Single Point Positioning (SPP) and Precise Point Positioning (PPP) techniques, comparing their accuracy and assessing the impact of observation duration on offline SPP accuracy. The instruments used include Hi Target v30 GNSS receiver and its accessories, Hi-Target Geomatics office software. The offline SPP data were obtained using the Hi-Target Geomatics office software. The PPP data were obtained using the offline SPP data that were converted to RINEX file and uploaded to CSRS-PPP online platform. Observations were carried out in 1 hour and 2 hours on different points respectively. The comparisons were done and the various charts were plotted as well to analyze the process. The results showed that offline SPP achieves higher accuracy with error ranges of 0.36m to 1.18m when observations last for 2-hour and between 1.67m to 3.48m for when observations last for 1-hour. The study demonstrates that longer observation periods improve offline GNSS SPP accuracy thereby reducing noise and multipath effects. The findings have significant implications for surveying and mapping applications in remote areas, where accurate positioning is crucial but without internet services.

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.

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.

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.

The tropospheric delay is one of the main error sources that degrades the accuracy of Global Navigation Satellite Systems (GNSS) Single Point Positioning (SPP). Although an empirical model is usually applied for correction and thereby to improve the positioning accuracy, the residual tropospheric delay is still drowned in measurement noise, and cannot be further compensated by parameter estimation. How much this type of residual error would sway the SPP positioning solutions on a global scale are still unclear. In this paper, the biases on SPP solutions introduced by the residual tropospheric delay when using nine conventionally Zenith Tropospheric Delay (ZTD) models are analyzed and discussed, including Saastamoinen+norm/Global Pressure and Temperature (GPT)/GPT2/GPT2w/GPT3, University of New Brunswick (UNB)3/UNB3m, European Geostationary Navigation Overlay System (EGNOS) and Vienna Mapping Functions (VMF)3 models. The accuracies of the nine ZTD models, as well as the SPP biases caused by the residual ZTD (dZTD) after model correction are evaluated using International GNSS Service (IGS)-ZTD products from around 400 globally distributed monitoring stations. The seasonal, latitudinal, and altitudinal discrepancies are analyzed respectively. The results show that the SPP solution biases caused by the dZTD mainly occur on the vertical direction, nearly to decimeter level, and significant discrepancies are observed among different models at different geographical locations. This study provides references for the refinement and applications of the nine ZTD models for SPP users.

On the enhanced detectability of GPS anomalous behavior with relative entropy

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.

The code-phase divergences, which are minimal for GPS, GLONASS, and Galileo satellites, are commonly found in BeiDou Navigation Satellite System (BDS) Geostationary Orbit (GEO), Inclined GeoSynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. Several precise positioning applications which use code observations are severely affected by these code biases. We present an analysis of code bias based on multipath (MP) combination observations. To mitigate the effect of BDS code bias on precise positioning, we proposed a periodical correction method using a low-pass filter for BDS GEO, IGSO and MEO satellites. The auto-correlation of MP series over long periods is analyzed to obtain the periods of the dominant repeating components for three types of BDS satellites. The periods of the dominant daily repeating components are close to 86,160 s for BDS GEO and IGSO satellites while 603,120 s for MEO satellites. The zero phase-shift low-pass filter was used to extract the low-frequency components of MP series and then low-frequency components are applied to mitigate the code bias periodically. The developed correction methods can make a more remarkable improvement for the accuracy of MP series, compared to the current elevation-dependent correction models. Data sets collected at 50 Global Navigation Satellite System (GNSS) ground stations including 15 of the International GNSS Monitoring and Assessment System (iGMAS) and 35 of the Multi-GNSS Experiment (MGEX) stations are employed for this study. To analyze the influence of code bias on precise positioning and validate the effectiveness of the correction methods, some applications such as single point positioning (SPP), wide-lane (WL) ambiguity analysis and Uncalibrated Phase Delays (UPDs) estimation are conducted. After applying the proposal correction method to the code observations, SPP solutions outperform the uncorrected ones in term of positioning accuracy. The positioning accuracy decreased by 0.28 and 0.1 m in the north and east components and the improvements are more significant for the U components decreased by 0.42 m. In addition, the systematic variations of Melbourne-Wübbena (MW) combination are greatly removed and the convergence time of the MW series are decreased. Moreover, significant improvement is also achieved in terms of the usage rate and residuals of UPDs estimation.

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.

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

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.

Receivers able to track satellites belonging to different GNSSs (Global Navigation Satellite Systems) are available on the market. To compute coordinates and velocities it is necessary to identify all the elements that contribute to interoperability of the different GNSSs. For example the timescales kept by different GNSSs have to be aligned. Receiver-specific biases, or firmware-dependent biases, need to be calibrated. The reference frame used in the representation of the orbits must be unique. In this paper we address the interoperability issues from the standpoint of a Single Point Positioning (SPP) user, i.e., using pseudoranges and broadcast ephemeris. The biases between GNSSs timescales and receiver-dependent biases are analyzed for a set of 31 MGEX (Multi-GNSS Experiment) stations over a time span of more than three years. Time series of biases between timescales of GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), Galileo, BeiDou, QZSS (Quasi-Zenith Satellite System), SBAS (Satellite Based Augmentation System) and NAVIC (Navigation with Indian Constellation) are investigated, in addition to the identification of events like discontinuity of receiver-dependent biases due to firmware updating. The GPS broadcast reference frame is shown to be aligned to the one (IGS14) realized by the precise ephemeris of CODE (Center for Orbit Determination in Europe) to within 0.1 m and 2 milliarcsec, with values dependent on whether IIR-A, IIR-B/M or IIF satellite blocks are considered. Larger offsets are observed for GLONASS, up to 1 m for GLONASS K satellites. For Galileo the alignment of the broadcast orbit to IGS14/CODE is again at the 0.1 m and several milliarcsec level, with the FOC (Full Operational Capability) satellites slightly better than IOV (In Orbit Validation). For BeiDou an alignment of the broadcast frame to IGS14/CODE comparable to GLONASS is observed, regardless of whether IGSO (Inclined Geosynchronous Orbit) or MEO (Medium Earth Orbit) satellites are considered. For all satellites, position differences according to the broadcast ephemeris relative to IGS14/CODE orbits are projected to the radial, along-track and crosstrack triad, with the largest periodic differences affecting mostly the along track component. Sudden discontinuities at the level of up to 1 m and 2–3 ns are observed for the along-track component and the satellite clock, respectively. The time scales of GLONASS, Galileo, QZSS, SBAS and NAVIC are very closely aligned to GPS, with constant offsets depending on receiver type. The offset of the BeiDou time scale to GPS has an oscillatory pattern with peak-to-peak values up to 100 ns. To characterize receiver-dependent biases the average of six Septentrio receivers is taken as reference, and relative offsets of the other receiver types are investigated. These receiver-dependent biases may depend on the individual station, or for the same station on the update of the firmware. A detailed calibration history is presented for each multiGNSS station studied.

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.

Modern localization relies on satellite and terrestrial infrastructures (e.g., global navigation satellite system (GNSS) and crowd-sourced Wi-Fi), supporting critical applications ranging from autonomous navigation to life services. However, they are susceptible to multimodal location spoofing attacks, where various types of wireless signals are crafted in a coordinated manner to deceive the receiver position. Such attacks can cause severe disruptions, such as misguiding vehicles and enabling fraud. Although recent deep learning methods help detect GNSS spoofing and jamming, they require extensive labeled datasets, they are not designed for multimodal location spoofing, training is computationally intensive and centralized, thus raising privacy concerns due to the sensitive nature of location data. We propose FedRAIM, a novel federated learning framework designed for robust, efficient, and privacy-preserving multimodal location spoofing detection. The key FedRAIM elements are: training labels are autonomously generated through an extended receiver autonomous integrity monitoring (RAIM) algorithm, incorporating diverse opportunistic information, i.e., terrestrial infrastructure signals, onboard sensor data, and naturally GNSS signals. During federated training, local long short-term memory (LSTM) models are trained independently on client devices. Server then aggregates, using the FedAvg algorithm, these local model parameters, not data, into a global model, which is subsequently distributed back to the clients for iterative training. Experimental results on real-world multimodal spoofing datasets demonstrate that FedRAIM achieves an area under the curve (AUC) of up to 98.3%.

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 %.