Fault Detection And Exclusion Research Articles

Chip-scale atomic clock (CSAC) aided GNSS in urban canyons

In urban canyons, the reflections and obstructions of Global Navigation Satellite System (GNSS) signals frequently lead to significant errors in measurements, the number of which can be larger than that of the correct measurements. This leads to a severe degradation of GNSS performance in urban canyons. Various fault detection and exclusion (FDE) algorithms have been developed to cope with these outliers caused by multipath effects. Most of these FDE algorithms check the consistency among measurements. However, in urban canyons, their effectiveness is significantly compromised by the lack of fault-free measurements. There is an urgent need to develop new constraints for enhancing GNSS FDE performance. In recent years, the advent of Chip-Scale Atomic Clock (CSAC), known for their affordability and high frequency stability, offers a promising solution for accurately predicting receiver clock errors. Additionally, using city maps to establish height constraints is another way to increase redundancy. The purpose of this study is to improve the GNSS positioning accuracy in urban canyons with the aid of CSAC and city map data. A novel FDE algorithm is developed to search for positions through the constraints of height and receiver clock. Extensive tests were conducted in urban canyons to evaluate the performance of the system. Results showed that the positioning accuracy can be improved from tens of meters to less than 6 m.

GNSS Fault Detection and Exclusion (FDE) Under Sidewalk Constraints for Pedestrian Localization in Urban Canyons

GNSS Fault Detection and Exclusion (FDE) Under Sidewalk Constraints for Pedestrian Localization in Urban Canyons

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

A NOVEL GPS FAULT DETECTION AND EXCLUSION ALGORITHM AIDED BY IMU AND VO DATA FOR VEHICLE INTEGRATED NAVIGATION IN URBAN ENVIRONMENTS

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

A new GNSS outlier mitigation method for GNSS/INS integrated system

High-precision positioning with global navigation satellite systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective fault detection and exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines autoregressive integrated moving average (ARIMA) model and multilayer perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle’s location for several future steps. Then, a dissimilarity-based method of dynamic time warping measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.

An Efficient Fault Detection and Exclusion Method for Ephemeris Monitoring

The ephemeris fault needs to be detected and mitigated in the ground-based augmentation system to provide precision approach for the aircraft. In the current fault detection and exclusion (FDE) method, the double-differenced carrier phase (DDCP) observation is used as a test statistic to detect a faulty satellite caused by an ephemeris fault, taking advantage of the residual spatial gradient. However, the current FDE method cannot distinguish whether the fault comes from a reference satellite (RS) or a non-reference satellite (NRS) in DDCP. One way to address this issue is to pre-validate the RS before it can be used to form a DDCP test statistic for detecting ephemeris fault on the NRS. The RS is pre-validated using the previous ephemeris for any newly acquired and re-acquired satellite. This method is developed in detail to present the shortcomings. A more efficient FDE method using multiple hypothesis testing to detect ephemeris fault on both the RS and NRS simultaneously in real time is proposed. Moreover, to facilitate the application in integrity monitoring, the test risks and minimum detectable error are analyzed. The numerical results of the proposed FDE method show an improved performance in detecting ephemeris fault on the RS and a comparable performance on the NRS compared with the current FDE method.

Fault-Tolerant Cooperative Positioning Based on Hybrid Robust Gaussian Belief Propagation

This paper proposes a hybrid robust Gaussian belief propagation (HRGBP) as a fault-tolerant cooperative positioning (CP) system that can be used to support cooperative intelligent transportation applications. For fault-tolerant state estimation, it is well known that fault detection and exclusion (FDE) based methods and Huber’s M-estimation based methods have their own drawbacks when facing different forms of observation outliers, or faults. To solve this problem, our proposed HRGBP uses an interactive multiple model (IMM) framework to fuse these two strategies, which combines the advantages of both methods without their drawbacks. HRGBP can fully exploit the message passing process to mitigate the biased estimates, which further improves the system’s fault-tolerant robustness. HRGBP can be further adapted to fuse more fault-tolerant strategies to improve the robustness of the CP system, and be extended to other factor graph-based methods. Here, we evaluate HRGBP for observations from visual landmark range and bearing, neighboring vehicle range and bearing, and odometer. Our evaluations show that HRGBP outperforms other state-of-the-art CP methods.

A Novel GNSS Fault Detection and Exclusion Method for Cooperative Positioning System

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

With the rapid development of safety–critical applications of Intelligent Transportation Systems, Global Navigation Satellite System (GNSS) fault detection and exclusion (FDE) methods have made navigation systems increasingly reliable. However, in multi-fault cases of urban environments, FDE methods generally demand massive calculations and have a high risk of missed detection and false alarm. To deal with this issue, we proposed a factor set-based FDE algorithm for integrating GNSS and Inertial Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterward, the FDE results are validated by missed-detection and false-alarm checks. The missed-detection check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the vehicle's position, velocity and attitude. The proposed algorithm improves both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the accuracy improvements of the velocity and the heading are over 20% and 50%, respectively.

Implementation and analysis of fault grouping for multi-constellation advanced RAIM

The aviation community is pursuing multi-constellation Advanced Receiver Autonomous Integrity Monitoring (ARAIM) to offer safe aircraft navigation services. The computational issue of ARAIM becomes critical when multiple constellations are involved and Fault Exclusion (FE) is enabled. This paper proposes an implementation of fault grouping to improve the efficiency of multi-constellation ARAIM and to benefit navigation performance. To potentially accommodate most ARAIM services, we consider both ARAIM Fault Detection (FD) and ARAIM Fault Detection and Exclusion (FDE) scenarios. In addition, given the difference between Vertical ARAIM (V-ARAIM) and Horizontal ARAIM (H-ARAIM), the implementation steps of fault grouping for V-ARAIM and H-ARAIM are respectively described. More importantly, unlike most of the prior approaches that were limited to dual-constellation scenarios, our implementation can support up to four constellations. The implementation of fault grouping is evaluated using multiple sets of simulations, which are carried out as a function of the number of constellations, prior fault probabilities, error models, and operational services. The results suggest that in most cases, the proposed implementation of fault grouping can effectively reduce the ARAIM computational load while benefiting or maintaining the navigation performance.

Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

To achieve higher automation level of vehicles defined by the Society of Automotive Engineers, safety is a key requirement affecting navigation accuracy. We apply Light Detection and Ranging (LiDAR) as a main auxiliary sensor and propose LiDAR-based Simultaneously Localization and Mapping (SLAM) approach for Positioning, Navigation, and Timing. Furthermore, point cloud registration is handled with 3D Normal Distribution Transform (NDT) method. The initial guess of the LiDAR pose for LiDAR-based SLAM comes from two sources: one is the differential Global Navigation Satellite System (GNSS) solution; the other is Inertial Navigation System (INS) and GNSS integrated solution, generated with Extended Kalman Filter and motion constraints added, including Zero Velocity Update and Non-Holonomic Constraint. The experiment compares two initial guesses for scan matching in terms navigation accuracy. To emphasize the importance of a multi-sensor scheme in contrast to the conventional navigation method using the stand-alone system, the tests are conducted in both open sky area and GNSS signal block area, the latter might cause Multipath and Non-Line-Of-Sight effects. To enhance the navigation accuracy, the Fault Detection and Exclusion (FDE) mechanism is applied to correct the navigation outcome. The results show that the application of NDT and FDE for INS/GNSS integrated system can not only reach where-in-lane level navigation accuracy (0.5 m), but also enable constructing the dynamic map.

Euclidean Distance Matrix-Based Rapid Fault Detection and Exclusion

<h3>Abstract</h3> Faulty signals from global navigation satellite systems (GNSSs) often lead to erroneous position estimates. A variety of fault detection and exclusion (FDE) methods have been proposed in prior research to both detect and exclude faulty measurements. This paper introduces a new technique for the FDE of GNSS measurements using Euclidean distance matrices. After a brief introduction to Euclidean distance matrices, both the detection and exclusion strategy is explained in detail. Euclidean distance matrix-based FDE is verified in two separate real-world data sets and proven to accurately detect and exclude GNSS faults on an average of 1.4-times faster than residual-based FDE and 70-times faster than solution separation FDE.

Multi-layered Multi-Constellation Global Navigation Satellite System Interference Mitigation

<h3>Abstract</h3> Several layers of defense can be implemented in a global navigation satellite system (GNSS) receiver to improve its performance in the presence of interference. These layers include the use of pre-correlation mitigation techniques, post-correlation quality indicators to screen measurements, and fault detection and exclusion (FDE) at the position solution level. This paper provides a characterization of the interactions between these layers of interference mitigation and a measurement quality check. Data collected in the presence of increasing levels of jamming were processed using different interference mitigation techniques, including robust interference mitigation (RIM) and the adaptive notch filter (ANF). A software defined radio (SDR) approach was adopted and measurements were generated by considering five interference-mitigation techniques. Position solutions were then computed using a forward-backward approach for receiver autonomous integrity monitoring (RAIM). Signals from GPS, Galileo, and Beidou were processed and both single and dual-constellation solutions were analyzed. The analysis revealed that interference mitigation allows the receiver to track a larger number of signals even in the presence of high levels of jamming power. This increased measurement availability was then effectively exploited by RAIM techniques to provide more reliable solutions. Measurements from several constellations further improved the reliable availability of the position solutions.

RAIM Fault Detection and Exclusion with Spatial Correlation for Integrity Monitoring

For safety-of-life applications used together with global navigation satellite systems, such as in civil aviation and autonomous driving, integrity is of paramount importance. Integrity monitoring protects the user safety based on two general approaches: mitigating large signal failures and bounding the residual errors. The fault detection and exclusion (FDE) of faulty satellites is part of the former approach. In the classical integrity monitoring algorithms in civil aviation, the use of test statistics based on least squares residuals relies on the assumption that the observations from different satellites are independent. When applied to urban environments with spatial correlation introduced by large multipath errors, a review of the FDE method is needed. With the optimal FDE method defined as the one with minimized integrity risk, we propose two optimization criteria for use in fault detection and fault exclusion. The optimal test statistics were obtained by analytical derivation for cases with and without correlations among different satellites. This method was theoretically compared with another commonly used test statistic using the minimum detectable bias, and it was numerically compared using the horizontal protection level under the scenario of advanced receiver autonomous integrity monitoring. The optimal test produces less conservative protection level results, and its advantage is especially obvious when the geometries are weak, or when the correlation coefficients among the satellites are high.

Adaptive Hybrid Robust Filter for Multi-Sensor Relative Navigation System

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.

A Dual w-Test Based Quality Control Algorithm for Integrated IMU/GNSS Navigation in Urban Areas

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively.

A modified between‐receiver single‐difference‐based fault detection and exclusion algorithm for real‐time kinematic positioning

With the increasing number of available satellites from multi-global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between-receiver single-differencing (SD)-based FDE algorithm for real-time kinematic (RTK) positioning. First, a modified between-receiver SD model-based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double-differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault-resolved ambiguities. To verify the modified SD model-based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time-to-first-fix, ambiguity-fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity-fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD-based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE.

Resilience Monitoring for Multi-Filter All-Source Navigation Framework With Assurance

<h3>Summary</h3> The Autonomous and Resilient Management of All-source Sensors (ARMAS) framework monitors residual-space test statistics across unique sensor-exclusion banks of filters (known as <i>subfilters</i>) to provide a resilient, fault-resistant all-source navigation architecture with assurance. A critical assumption of this architecture, demonstrated in this paper, is fully overlapping state observability across all subfilters. All-source sensors, particularly those that only provide partial state information (altimeters, TDoA, AOB, etc.), do not intrinsically meet this requirement. This paper presents a novel method to monitor real-time overlapping position state observability and introduces an observability bank within the ARMAS framework, known as stable observability monitoring (SOM). SOM uses a monitoring-epoch stability analysis to provide an intrinsic awareness to ARMAS of the capabilities of the fault detection and exclusion (FDE) functionality. We define the ability to maintain consistent all-source FDE to recover failed sensors as <i>navigation resilience</i>. A resilient FDE capability is one that is aware of when it requires more sensor information to protect the consistency of the FDE and integrity functions from corruption. SOM is the first demonstration of such a system for all-source sensors that the authors are aware of. A multi-agent 3D environment simulating both GNSS and position and velocity alternative navigation sensors was created and individual GNSS pseudorange sensor anomalies are utilized to demonstrate the capabilities of the novel algorithm. This paper demonstrates that SOM seamlessly integrates within the ARMAS framework, provides timely prompts to augment new sensor information from other agents, and indicates when framework stability and preservation of all-source navigation integrity are achieved.

Effect of Erroneous Observations in GPS-based LEO Satellite Orbit Determination

Performance of an EKF-based Orbit Determination (OD) algorithm using Global Positioning System (GPS) measurements is examined under erroneous observation conditions. A generalized formulation of a dynamic orbit model is designed to predict the satellite states in an Earth-Centered Earth-Fixed (ECEF) frame. A suitable orbit interpolation method is then chosen for interpolating the GPS satellite position at the measurement time tag before finding the geometric range between the GPS and the LEO satellites. Thereafter, the commonly used Extended Kalman Filter (EKF) algorithm is implemented for the nonlinear OD problem. The proposed algorithm is tested for the precision in OD of two LEO satellites, namely GRACE and Megha-Tropiques based on the available raw GPS measurements in the Receiver Independent Exchange (RINEX) observation files. For the GRACE satellite, the efficacy of the present OD algorithm is validated by comparing the estimated and publicly available precise orbit information. The Megha-Tropiques satellite, however, does not have precise orbit information. Furthermore, it is observed that the RINEX file has several erroneous observations as detected by the Fault Detection and Exclusion (FDE) algorithm in-built in the OD algorithm. A comparison of the estimated satellite position with that obtained from the standard software GNSS-Lab Tool (gLAB) shows that the accuracy of the position estimates varies proportionally with the number of erroneous observations of the Megha-Tropiques satellite.

Multi-Layer Defences for Robust GNSS Timing Retrieval.

A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including: pre-correlation interference mitigation techniques, post-correlation measurement screening and Fault Detection and Exclusion (FDE) at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicators (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions: in this respect T-RAIM can provide the required level of reliability.