Global Navigation Satellite System Integrity Research Articles

Dynamic rail wear measurement: integration of RTK GNSS, IMU, and laser

Currently, laser sensors are widely utilized in railway systems for monitoring rail wear. However, the lack or insufficiency of rail waist data poses a challenge to accurately match profiles and calculate wear. In this paper, we propose a novel approach for dynamic rail wear monitoring that comprises three major modules: a filtering methodology to smooth rail profile data, fine compensation to calibrate the angle distortion, and fast profile alignment. Initially, the AF technique and Kalman filter are applied to reconstruct the profile. This is followed by integrating real-time kinematic global navigation satellite system, inertial measurement unit, and laser to precisely correct the rail profile dynamic distortion. Additionally, genetic algorithms-least squares method is used for rapid determination of the R20mm center of the profile. Rigorous testing and trials have demonstrated that our proposed approach achieves precision as low as 0.03 mm with significant potential in dynamic rail wear monitoring.

Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

On the role of lidar measurements in speeding up precise point positioning convergence

Global navigation satellite system (GNSS) and light detection and ranging (lidar) are well known to be complementary for vehicle positioning in urban canyons, where GNSS observations are prone to signal blockage and multi-path. As one of the most common carrier-phase-based precise positioning techniques, precise point positioning (PPP) enables single-receiver positioning as it utilizes state-space representation corrections for satellite orbits and clocks and does not require a nearby reference station. Yet PPP suffers from a long positioning convergence time. In this contribution, we propose to reduce the PPP convergence using an observation-level integration of GNSS and lidar. Lidar measurements, in the form of 3D keypoints, are generated by registering online scans to a pre-built high-definition map through deep learning and are then combined with dual-frequency PPP (DF-PPP) observations in an extended Kalman filter implementing the constant-velocity model that captures the vehicle dynamics. We realize real-time PPP (RT-PPP) in this integration using the IGS real-time service products for vehicle positioning. Comprehensive analyses are provided to evaluate different combinations of measurements and PPP corrections in both static and simulated kinematic modes using data captured by multiple receivers. Experimental results show that the integration achieves cm-level accuracy and instantaneous convergence by using redundant measurements. Accordingly, for classical PPP accuracy of 10 cm and convergence within minutes, respectively, lidar input is only required once every 10 s.

GNSS integrity risk evaluation in the position domain based on the generalized Pareto distribution

Integrity monitoring of global navigation satellite systems (GNSSs) is designed to protect against extremely rare hazardous events, characterized by an integrity risk with a very low probability. The traditional integrity risk evaluation is restricted simultaneously by non-Gaussian measurement errors and impractical time consumption. Based on extreme value theory, a generalized Pareto distribution (GPD)-based integrity risk evaluation method in the position domain is proposed to estimate the upper bound of the integrity risk. In order to account for the GPD modeling error and estimation error, conservatism of the proposed GPD-based integrity risk evaluation is obtained by imposing model-driven and data-driven overbounding. Simulation results from four typical heavy-tailed distributions have shown that conservative and tight bound integrity risk results can be achieved. Furthermore, real-world European Geostationary Navigation Overlay Service measurements experiment has shown that the integrity risk evaluation resulting from the proposed method is at least one order less than the traditional evaluation method, which is consistent with official publications.

A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering

One of the core issues of mobile measurement is the pose estimation of the carrier. The classic Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) integrated navigation scheme has high positioning accuracy but is vulnerable to Global Navigation Satellite System (GNSS) signal occlusion and multipath effect. Simultaneous Localization and Mapping (SLAM) is not affected by signal occlusion, but there are problems such as error accumulation and scene degradation. The multi-sensor fusion scheme combining the two technologies can effectively expand the scene coverage and improve the positioning accuracy and system robustness. However, the current scheme has some limitations. On the one hand, GNSS plays a less important role in most SLAM systems, only for initialization or as a closed-loop factor and other auxiliary work. On the other hand, in the fusion method, most of the current systems only use filtering or graph optimization, without taking into account the advantages of both. Aiming at pose estimation for mobile carriers, this paper combines the advantages of the global optimization of the factor graph and the local optimization of filtering and proposes a GNSS-IMU-LiDAR Constraint Kalman Filter (abbreviated as GIL-CKF), which has the characteristics of full coverage and effectively improving absolute accuracy and high output frequency. The scheme proposed in this paper consists of three parts. Firstly, an extensible factor map is used to fuse the positioning information from different sources, including GNSS, IMU, LiDAR, and a closed-loop map, to maintain a high-precision SLAM system, and the output is used as Multi-Sensor-Fusion-Odometry (MSFO). Then, the scene is divided according to the GNSS quality factor, and a Scene Optimizer (SO) is designed to filter GNSS pose and MSFO. Finally, the results are input into the Extended Kalman Filter (EKF) together with the original IMU data for further optimization and smoothing. The experimental results show that the integration of high-precision GNSS positioning information with IMU, LiDAR, a closed-loop map, and other information through the factor map can effectively suppress error accumulation and improve the overall accuracy of the SLAM system. The SO based on GNSS indicators can fully retain high-precision GNSS positioning information, effectively play their respective advantages of filtering and graph optimization, and alleviate the conflict between global and local optimization. SO with EKF filtering furthers optimization, can improve the output frequency, and smooth the trajectory. GIL-CKF can effectively improve the accuracy and robustness of pose estimation and has obvious advantages in multi-sensor scene complementarity, partial road section accuracy improvement, and high input frequency.

Tightly Coupled Integration of GNSS, INS, and LiDAR for Vehicle Navigation in Urban Environments

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, the global navigation satellite system (GNSS) is recognized as an important approach for worldwide positioning services. However, its application is limited in urban areas due to severe signal attenuation, reflections, and blockages. Inertial navigation system (INS) can provide high-precision navigation outputs within a short period, but its accuracy suffers from error accumulation, especially when equipped with the low-cost microelectromechanical system (MEMS) inertial measurement units (IMUs). In addition, light detection and ranging (LiDAR) is becoming more common as an option in vehicles, which can detect rich geometric information in the environment for ego-motion estimation. Aiming at taking advantage of the complementary characteristics of these onboard technologies to navigate in urban environments, a tightly coupled multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system is proposed. We also develop an LiDAR sliding-window plane-feature tracking method to further improve navigation accuracy and computational efficiency. The performance of the proposed integrated system was evaluated in vehicular experiments with different GNSS observation conditions. Results indicate that our proposed GNSS/INS/LiDAR integration can maintain submeter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, and 64.2%) compared to traditional GNSS/INS integration. Moreover, the plane-feature tracking method is proved to outperform traditional point-to-line and point-to-plane scan matching in terms of accuracy and efficiency.

Continuous and Precise Positioning in Urban Environments by Tightly Coupled Integration of GNSS, INS and Vision

Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenarios. To fill this gap, this letter proposes to exploit the complementariness of the global navigation satellite system (GNSS), inertial measurement unit (IMU) and vision via a tightly coupled integration method, aiming to achieve continuous and accurate navigation in urban environments. Specifically, the raw GNSS carrier phase and pseudorange measurements, IMU data, and visual features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF) to make full use of the multi-sensor information and reject potential outlier measurements. Furthermore, the widely used high-precision GNSS models including precise point positioning (PPP) and real-time kinematic (RTK) are unified in the proposed integrated system to increase usability and flexibility. We validate the performance of the proposed method on several challenging datasets collected in urban canyons and compare against the loosely coupled and state-of-the-art methods.

Tight Integration of GNSS and Static Level for High Accuracy Dilapidated House Deformation Monitoring

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring.

Loosely Coupled GNSS and IMU Integration for Accurate i-Boat Horizontal Navigation

Global Navigation Satellite System (GNSS) has been widely utilized as a navigation solution for a mobile vehicle, yet stand-alone GNSS is unable to achieve sufficient accuracy in some applications. For example, in aquatic environment, the accuracy position of Unmanned Surface Vehicle (USV) is affected by the wind, current, and waves dynamics. In this case, the integration of GNSS and Inertial Measurement Unit (IMU) is an approach that can be used to support USV localization to achieve an accurate navigation solution. This study integrates GNSS and IMU using Extended Kalman Filter (EKF) to process loosely coupled integration. The integration results show that the estimated x-position is 0.3058 m accurately and the y-position has an accuracy of 0.2780 m. Then, the loosely coupled integration results of EKF were compared with Unscented Kalman Filter (UKF) simulation in the previous studies. The integration of GNSS and IMU using EKF produces a higher RMSE value of 2D position and attitude angle than UKF Scenario I. However, due to the smoothing process, EKF can provide a visually smoother trajectory than UKF, although it has a significant difference from the observed trajectory. On the other hand, the linear velocity estimated by EKF shows better stability compared to UKF in both Scenario I and II.

IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.

Global Navigation Satellite System (GNSS) and other geospatial tools for various applications

Nowadays the Global Satellite Navigation Systems (GNSS) play a fundamental role in many areas as civil aviation, maritime and land navigation and geomatics, owing to the ability to provide worldwide, tridimensional, all-weather position, and velocity and time synchronization. The final product of the Global Navigation Satellite System exercise is the Three - Dimensional coordinates (3D) of the receiving station. These coordinates have been found reliable for most geospatial applications. However, some applications in data management required other information apart from geodetic coordinates. Hence; GNSS has been integrated with other methods of data acquisition to improve the quality of data for various applications. These have helped in solving a lot of problems where individual methods failed. This paper examines some satellite-based systems and reports the integration of GNSS with other data acquisition tools such as geodetic levelling, Remote Sensing, Geographic Information System (GIS), Inertial Navigation System (INS) and many others. In some cases, the synergy had resulted in other satellite or payload programs such as Gravity Recovery and Climate Experiment (GRACE), while it has improved the GNSS applications in so many areas. GNSS integration is therefore recommended for any application that requires the use of the location.

Code phase tracking error based autonomous integrity monitoring for GNSS/INS ultra-tightly integrated system

The ultra-tight integration of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) may suffer from faults in GNSS, INS and integration filter. Both the faults occurred at the current epoch and at previous epochs affect the position solution deviation of ultra-tight integration at the current epoch, and the effect is reflected in GNSS code phase tracking errors. In this paper, a new autonomous integrity monitoring algorithm is proposed for the federated GNSS/INS ultra-tight integrations in which the code phases of local GNSS signal replicas for GNSS signal tracking are completely derived from the integrated navigation solution. In the proposed algorithm, GNSS code phase tracking errors are employed to construct the test statistics for fault detection through a MHSS (Multiple Hypothesis Solution Separation) method improved from the classical MHSS used in ARAIM (Advanced Receiver Autonomous Integrity Monitoring). With the improved MHSS, the proposed algorithm can account for GNSS nominal biases, multiple GNSS faults and the faults in INS and integration filter. The protection levels under multiple hypothesis are also derived. A semi-physical simulation of the federated ultra-tight integration of GPS (Global Positioning System) and INS in airplane approaching case and a vehicle-based field test of the federated ultra-tight integration of GPS, Galileo and INS are conducted to validate the proposed algorithm. The experiments show that the designed fault detection can effectively detect the occurred faults and the derived protection levels can correctly bound the caused position solution error.

Analysis of GNSS, Hydroacoustic and Optoelectronic Data Integration Methods Used in Hydrography.

The integration of geospatial data in hydrography, performed using different measurement systems, involves combining several study results to provide a comprehensive analysis. Each of the hydroacoustic and optoelectronic systems is characterised by a different spatial reference system and the method for technical implementation of the measurement. Therefore, the integration of hydrographic data requires that problems in selected fields of electronics, geodesy and physics (acoustics and optics) be solved. The aim of this review is to present selected fusion methods applying the data derived from Global Navigation Satellite System (GNSS), Real Time Kinematic (RTK) measurements, hydrographic surveys, a photogrammetric pass using unmanned vehicles and Terrestrial Laser Scanning (TLS) and compare their accuracy. An additional goal is the evalution of data integration methods according to the International Hydrographic Organization (IHO) S-44 standard. The publication is supplemented by implementation examples of the integration of geospatial data in the Geographic Information System (GIS). The methods described indicate the lack of a uniform methodology for data fusion due to differences in both the spatial reference systems and the techniques used. However, the integration of hydroacoustic and optoelectronic data allows for high accuracy geospatial data to be obtained. This is confirmed by the methods cited, in which the accuracy of integrated geospatial data was in the order of several centimetres.

Detection of GNSS no-line of sight signals using LiDAR sensors for intelligent transportation systems

The reliability and robustness of positioning systems in urban and suburban environments are intrinsic. This is obvious following the continuous increase of Intelligent Transportation Systems (ITS) applications in such challenging environments. Global Navigation Satellite Systems (GNSS) represent the primary positioning technique used for navigation purposes in these applications, which can be satisfying in open-sky areas. However, GNSS cannot provide the same level of navigation performance in urban environments. One of the main reasons for this is the No-Line of Sight (NLOS) signals. In this study, the integration of GNSS and Light Detection and Ranging (LiDAR) sensors is exploited, and a new algorithm is proposed for the detection of NLOS signals. Real field data are used to test and validate the proposed strategy and algorithm. Phase-smoothed code observations are employed to evaluate the accuracy improvement after excluding the NLOS observations. The results show that the horizontal direction's positional accuracy can be improved significantly after applying the proposed algorithm. This improvement reaches 10.403 m with a mean value of 2.162 m (62.2% improvement) over all epochs with detected NLOS signals. After analysing this improvement in the Cross-Track (CT) and Along-Track (AT) directions, it is found that the accuracy improvement reaches 8.641 m with a mean value of 1.699 m in the CT direction and 6.879 m with a mean value of 1.303 m in the AT direction.

A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography

Global Navigation Satellite System (GNSS) tomography has proved its potential for sensing the three-dimensional (3D) distribution of atmospheric water vapor. Although the standard tomography model has improved, the geometry defect of GNSS acquisition remains a key problem and thus becomes the focus of this research. Thanks to the availability of high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water vapor (PWV) maps, the integration of GNSS and MODIS measurements for tropospheric tomography system is introduced and discussed to address this problem. A methodology based on the node tomography model is developed to exploit the MODIS signals, which overcomes the disadvantage of the standard voxel model in combining the MODIS observations. Three experimental schemes based on MODIS data and simultaneous GNSS observations over the Xuzhou area are implemented to validate the proposed approach. The results show that the mean number of newly crossed voxels (i.e., punctured only by the MODIS signals) increases from 2 in the top layer to 24 in the first layer. Consequently, the average number of total crossed voxels is increased from 272 to 447, which highlights the contribution of the MODIS observations to pass through the empty voxels. Besides, the mean number of tomographic observation equations is enhanced by 35.71% when the MODIS signals are combined. Two-dimensional (2D) profiles of water vapor from radiosonde and 3D distributions of it from ERA5 data are considered to validate the proposed method. It is noted that when using the proposed approach, the mean root-mean-square error (RMSE) of the 2D tomographic profiles is decreased by a value of about 1.06 g/m3 and the overall accuracy of the 3D water vapor distribution is improved by 30.40%. Both the 2D and 3D validations demonstrate the satisfactory performance of the new integrated method to optimize the tomographic water vapor field.

Analysis of BDS GEO satellite multipath effect for GNSS integrity monitoring in civil aviation

With the great development of Global Navigation Satellite System (GNSS), multi-GNSS constellations (GPS, BDS, GLONASS, and Galileo) are able to provide users with more accurate positioning result. For civil aviation, to guarantee user’s safety, multi-constellation GNSS needs to meet the integrity requirement. Using conservative error models, Multiple Hypothesis Solution Separation (MHSS) Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is proposed to evaluate GNSS integrity. Current multipath error model in ARAIM algorithm is based on data of GPS Medium Earth Orbit (MEO) satellites. But BDS is a hybrid constellation. For BDS II, there has 5 Geosynchronous Earth Orbit (GEO) satellites. Previous studies have shown that the multipath effect of GEO satellites has statistical characteristics different from MEO satellites. Meanwhile, the multipath magnitude of GEO satellites is larger than that of MEO satellites. This paper mainly focuses on validating whether the multipath error model in ARAIM algorithm is conservative enough for GEO satellites. In this paper, Code Minus Carrier (CMC) residuals are calculated for BDS GEO dual-frequency signals. Then the Standard Deviation (STD) of CMC residuals can be conservatively estimated by bounded Cumulative Distribution Function (CDF). After eliminating interference from receiver noise, STD of GEO multipath can be obtained. Comparing the STD of GEO multipath effect with ARAIM multipath error model, a conclusion could be drawn that current multipath error model in ARAIM algorithm is no longer able to conservatively estimate the statistical characteristics of GEO satellites.

Integration of GNSS and BLE Technology With Inertial Sensors for Real-Time Positioning in Urban Environments

The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.

A hybrid integrity monitoring method using vehicular wireless communication in difficult environments for GNSS

Vehicular communication using the DSRC (Dedicated Short Range Communication) standard is becoming an enabling factor for a wide range of Cooperative Intelligent Transportation System (C-ITS) applications, especially those with high requirements of safety assurance. The location information of connected vehicles is fundamental, and significant for ensuring safe driving and preventing traffic accidents under complex and uncertain conditions. In order to alleviate the drawbacks of GNSS (Global Navigation Satellite System) based positioning due to satellite signal non-availability under difficult environments such as dense urban areas, a hybrid integrity monitoring technique is proposed. The range-rate measurement from the DSRC unit in the vehicle is employed to assist integrity monitoring of observable satellites, and the DSRC measurements are sent to the target vehicle through a vehicle-to-vehicle channel along with the identified state of neighboring vehicles. With the assistance from surrounding neighborhoods and a road map database, a virtual satellite ranging solution is possible to expand the measurements for enabling autonomous GNSS integrity monitoring and fault detection without the limitation of satellite visibility. Simulations conducted with different vehicle operation conditions and GNSS fault models demonstrate that the proposed solution outperforms conventional RAIM (Receiver Autonomous Integrity Monitoring) methods making it promising for many location-based cooperative applications.

GNSS Integrity Monitoring Schemes for Terrestrial Applications in Harsh Signal Environments

Global Navigation Satellite System (GNSS) integrity is defined as a measure of trust that can be placed in the correctness of the information supplied by the total system. Initially developed for safetycritical applications in the aeronautic domain, this concept has attracted more and more attention from terrestrial GNSS-based applications in recent years. The main problem of integrity monitoring for urban transport applications is related to GNSS signal degradation due to local effects such as multipath and non-line-of-sight reception. This article proposes two integrity monitoring schemes with two classes of approaches, based on either the snapshot weighted least-squares residual or sequential weighted extended Kalman filter innovation. The integrity monitoring schemes are mainly realized by two modules: accuracy enhancement, in which measurement errors are better characterized, and integrity monitoring, in which one of the fault detection and exclusion techniques, i.e., the Danish reweighting method, is applied. The results with real GPS data collected in urban canyons show that the proposed system can effectively improve positioning accuracy and guarantee system integrity.