Receiver Autonomous Integrity Monitoring Method Research Articles

Sequential advanced receiver autonomous integrity monitoring method considering the optimal sample size

The integrity monitoring performance of Receiver Autonomous Integrity Monitoring (RAIM) degrades when insufficient satellites are visible, which may cause RAIM to fail to support the approach procedure with vertical guidance service. We propose a sequential advanced receiver autonomous integrity monitoring (S-ARAIM) method to improve the integrity monitoring capability and availability. We first introduce a sample size to a dominant snapshot technique, advanced RAIM (ARAIM), to formulate cumulative test statistics for fault detection. The sample size is then determined to maximize the fault detection capability, including the response speed and sensitivity to minor faults. Meanwhile, the optimal sample size minimizes the undetected fault magnitude bound when no fault is declared. In this manner, S-ARAIM combines the advantage of snapshot RAIM and sequential RAIM in detecting severe pulse and small persistent faults. Additionally, we derive a protection level to envelop the estimate error under the constraints of continuity and integrity requirements. Experiments validate the fault detection performance under single-satellite-fault and dual-satellite-fault scenarios. Compared with least square residual RAIM and ARAIM, S-ARAIM improves the integrity availability by 45.91% and 5.64%, respectively.

Nonparametric estimation-based five-layer neural network RAIM with improved availability

The monitoring performance of receiver autonomous integrity monitoring (RAIM) is restricted when visible satellites are limited in challenging environments. For that, artificial neural network-based RAIM methods have been investigated to improve the detection efficacy. Nevertheless, their corresponding fault exclusion and protection level algorithms are hardly provided for integrity assessments. In this regard, a nonparametric estimation-based RAIM method (NE-RAIM) is investigated to support fault detection, exclusion, and protection level calculation in this paper, boosting the declined monitoring capacity caused by the decrease of visible satellites. We propose a classification variable and a dynamic sampling method based on the variance inflation theory and then obtain the regression of the classification variable using nonparametric estimation. In this way, a five-layer NE-RAIM neural network is constructed to enhance the detection capability further. We also provide a NE-RAIM-based fault exclusion strategy by analyzing the detection result vector. Meanwhile, a protection level algorithm is proposed to enable direct integrity and availability evaluation based on searching the worst-case scenario where the missed detection risk is maximized. Results show that NE-RAIM requires a minimum pseudorange bias of 35 m to realize 100% detection rates under all single-faulty-satellite modes. Compared with least-square RAIM and advanced RAIM, NE-RAIM improves overall 24 h availability by 59.30% and 4.52%, respectively.

Study on an optimized grouping RAIM method

Receiver Autonomous Integrity Monitoring (RAIM) method is widely used in the field of aviation navigation for its advantages of fast response, low cost and wide coverage. The performance of traditional RAIM is not as good as that of fault detection, and the calculation is complex. With the development of multi-constellation integrated positioning technology, the number of visible satellite is gradually increasing. Although the performance of traditional RAIM method has been improved, but it is not large, and the amount of calculation has increased greatly. In order to solve the above problems, an optimized grouping RAIM method is proposed. Through uniform grouping, fault detection can be directly used to complete fault identification, which reduces a lot of calculation and improves the efficiency and performance. The simulation results of graphics and data show that the algorithm is feasible and effective.

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a fault-free pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.

One‐dimensional traversal receiver autonomous integrity monitoring method based on maximum likelihood estimation for GNSS anti‐spoofing applications

By forging global navigation satellite system (GNSS) signals similar to authentic ones, a spoofer can make receivers track forged signals (spoofing signals) and generate wrong position, velocity and time results. Receiver autonomous integrity monitoring (RAIM) can be extended to the field of spoofing detection and exclusion (SDE). However, it is well known that when there are six or more signals and only one spoofing signal among them, RAIM can effectively exclude the spoofing signal. In this study, based on maximum likelihood estimation (MLE) theory and the idea of the traverse, one-dimensional traversal MLE-RAIM (TMRAIM) is proposed, which can exclude multiple spoofing signals. Theoretically, the influence of spoofing biases will be reflected in pseudorange residuals, and then affect the probability distribution of the parity vector. Through MLE deduction, the authors can find corresponding spoofing signals which are relevant with the maximum probability of the parity vector only once under the supposed number of spoofing signals. By just traversing the number of spoofing signals, TMRAIM can run effectively on real-time GNSS receivers with low complexity. The SDE ability and time complexity are analysed in detail and two field experiments are constructed. Experimental results demonstrate the method is feasible and effective for anti-spoofing applications.

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.

A Review of the Evolution of the Integrity Methods Applied in GNSS

The use of GNSS technologies has been spreading over time up to a point in which a huge diversity of applications require their use. Due to this demand, GNSS has turned into a more reliable technology, as multiple aspects of it have evolved. Integrity has become a vital aspect of being considered when using GNSS. The following document gathers and shows different aspects of integrity in terms of GNSS. The paper mainly focuses on the description of different receiver autonomous integrity monitoring methods. For this purpose, basic concepts and possible GNSS error sources (and their corresponding solutions) are introduced. Afterward, an explanation and a classification of the integrity monitoring techniques is given, where the fault detection and exclusion methods and different protection level computation formulas are analyzed.

An Enhanced RAIM Method for Satellite-Based Positioning Using Track Constraint

Integrity of global navigation satellite system (GNSS) positioning is one major concern for the GNSS-based railway train control systems due to critical safety requirements. In this paper, we propose a map-enhanced receiver autonomous integrity monitoring (RAIM) method for satellite-based positioning of railway trains, which could benefit from the autonomy of fault detection and exclusion (FDE) without relying on additional sensors in a location determination system (LDS). With the proposed RAIM solution, the spatial constraint from the fixed railway tracks is employed to predict the measurements from invisible satellites, which makes it possible of performing FDE even when insufficient satellites are visible to enable the conventional RAIM operation. We demonstrate that different faults in raw GNSS measurements can be identified and isolated by extracting immediate information from the state estimator. With a virtual satellite pseudorange generation mechanism, two-stage FDE architecture is proposed. A pseudorange consistency check logic in the measurement domain is adopted at a local stage. Furthermore, a filter-innovation-based RAIM strategy is utilized to realize a global test in the positioning domain under a cubature Kalman filter (CKF)-based sensor data fusion framework. The proposed method is verified by the field experiment and simulations and the results show the improvement to the availability and safety of GNSS-based positioning.

RAIM Algorithm Based on Fuzzy Clustering Analysis

With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means(FCM). And on the condition of single or two gross errors, the performance of hard or fuzzy clustering analysis were compared. As the results of the experiments, the fuzzy clustering method based on FCM principle could detect multiple gross error effectively, also achieved the quality control of single point positioning and ensured better reliability results.

Research on GNSS Receiver Autonomous Integrity Monitoring Method Based on M-Estimation

Receiver Autonomous Integrity Monitoring (RAIM) method is an effective means to provide integrity monitoring for users in time. In order to solve the misjudgment caused by the interference of gross error to the least squares algorithm, this paper proposes a RAIM method based on M-estimation for multiconstellation GNSS. Based on five programs, BDS, GPS/BDS, and GPS/BDS/GLONASS at the current stage, the future Beidou Global Navigation Satellite System, and the future GPS/BDS/GLONASS/Galileo system, the new RAIM method is compared with the traditional least squares method by simulation. The simulation results show that, with the increase of constellations, RAIM availability, fault detection probability, and fault identification probability will be improved. Under the same simulation conditions, the fault detection and identification probabilities based on M-estimation are higher than those based on least squares estimation, and M-estimation is more sensitive to minor deviation than least squares estimation.

On the enhanced detectability of GPS anomalous behavior with relative entropy

A standard receiver autonomous integrity monitoring (RAIM) technique for the global positioning system (GPS) has been dedicated to provide an integrity monitoring capability for safety-critical GPS applications, such as in civil aviation for the en-route (ER) through non-precision approach (NPA) or lateral navigation (LNAV). The performance of the existing RAIM method, however, may not meet more stringent aviation requirements for availability and integrity during the precision approach and landing phases of flight due to insufficient observables and/or untimely warning to the user beyond a specified time-to-alert in the event of a significant GPS failure. This has led to an enhanced RAIM architecture ensuring stricter integrity requirement by greatly decreasing the detection time when a satellite failure or a measurement error has occurred. We thus attempted to devise a user integrity monitor which is capable of identifying the GPS failure more rapidly than a standard RAIM scheme by incorporating the RAIM with the relative entropy, which is a likelihood ratio approach to assess the inconsistence between two data streams, quite different from a Euclidean distance. In addition, the delay-coordinate embedding technique needs to be considered and preprocessed to associate the discriminant measure obtained from the RAIM with the relative entropy in the new RAIM design. In simulation results, we demonstrate that the proposed user integrity monitor outperforms the standard RAIM with a higher level of detection rate of anomalies which could be hazardous to the users in the approach or landing phase and is a very promising alternative for the detection of deviations in GPS signal. The comparison also shows that it enables to catch even small anomalous gradients more rapidly than a typical user integrity monitor.

Integrity Risk Minimisation in RAIM Part 2: Optimal Estimator Design

This paper is the second part of a two-part research effort to find the optimal detector and estimator that minimise the integrity risk in Receiver Autonomous Integrity Monitoring (RAIM). Part 1 shows that for realistic navigation requirements, the solution separation RAIM method can approach the optimal detection region when using a least-squares estimator. This paper constitutes Part 2. It presents new methods to design Non-Least-Squares (NLS) estimators, which, in exchange for a slight increase in nominal positioning error, can substantially lower the integrity risk. A first method is formulated as a multi-dimensional minimisation problem, which directly minimises integrity risk, but can only be solved using a time-consuming iterative process. Parity space representations are then exploited to develop a computationally-efficient, near-optimal NLS-estimator-design method. Performance analyses for an example multi-constellation Advanced RAIM (ARAIM) application show that this new method enables significant integrity risk reduction in real-time implementations where computational resources are limited.

Integrity Risk Minimisation in RAIM Part 1: Optimal Detector Design

This paper describes the first of a two-part research effort to find the optimal detector and estimator that minimise the integrity risk in Receiver Autonomous Integrity Monitoring (RAIM). In this first part, a new method is established to determine a piecewise linear approximation of the optimal detection region in parity space. The paper presents examples suggesting that the optimal detection boundary lays in between that obtained using chi-squared residual-based RAIM, and that provided by Solution Separation (SS) RAIM, as one varies the alert limit requirement. In addition, these examples indicate that for realistic navigation requirements, the SS RAIM method approaches the optimal detection region. The SS RAIM detection tests will be employed in the second part of this work, which focuses on the design of non-least-squares estimators to reduce the integrity risk in exchange for a slight increase in nominal positioning error.

Research on GPS Receiver Autonomous Integrity Monitoring Algorithm In the Occurrence of Two-satellite Faults

Reliability is an essential factor for GPS navigation system. Therefore, an integrity monitoring is considered as one of the most important parts for a navigation system. GPS receiver autonomous integrity monitoring (RAIM) technique can detect and isolate fault satellite. Based on particle filter, a novel RAIM method was proposed to detect two-satellite faults of the GPS signal by using hierarchical particle filter. It can deal with any system nonlinear and any noise distributions. Because GNSS measurement noise does not follow the Gaussian distribution perfectly, the particle filter can estimate the posterior distribution more accurately. In order to detect fault, the consistency test statistics is established through cumulative log-likelihood ratio (LLR) between the main and auxiliary particle filters (PFs).Specifically, an approach combining PF with the hierarchical filter is used in the process of two-satellite faults. Through GPS real measurement, the performance of the proposed GPS two-satellite faults detection algorithm was illustrated. Some simulation results are given to evaluate integrity monitoring performance of the algorithm. Validated by the real measurement data, the results show that the proposed algorithm can successfully detect and isolate the faulty satellite in the case of non-Gaussian measurement noise.

The Proposal to “Snapshot” Raim Method for Gnss Vessel Receivers Working in Poor Space Segment Geometry

Abstract Nowadays, we can observe an increase in research on the use of small unmanned autonomous vessel (SUAV) to patrol and guiding critical areas including harbours. The proposal to “snapshot” RAIM (Receiver Autonomous Integrity Monitoring) method for GNSS receivers mounted on SUAV operating in poor space segment geometry is presented in the paper. Existing “snapshot” RAIM methods and algorithms which are used in practical applications have been developed for airborne receivers, thus two main assumptions have been made. The first one is that the geometry of visible satellites is strong. It means that the exclusion of any satellite from the positioning solution don’t cause significant deterioration of Dilution of Precision (DOP) coefficients. The second one is that only one outlier could appear in pseudorange measurements. In case of SUAV operating in harbour these two assumptions cannot be accepted. Because of their small dimensions, GNSS antenna is only a few decimetres above sea level and regular ships, buildings and harbour facilities block and reflect satellite signals. Thus, different approach to “snapshot” RAIM is necessary. The proposal to method based on analyses of allowable maximal separation of positioning sub-solutions with using some information from EGNOS messages is described in the paper. Theoretical assumptions and results of numerical experiments are presented.

A closed-loop EKF and multi-failure diagnosis approach for cooperative GNSS positioning

Current cooperative positioning with global navigation satellite system (GNSS) for connected vehicle application mainly uses pseudorange measurements. However, the positioning accuracy offered cannot meet the requirements for lane-level positioning, collision avoidance and future automatic driving, which needs real-time positioning accuracy of better than 0.5 m. Furthermore, there is an apparent lack of research into the integrity issue for these new applications under emerging driverless vehicle applications. In order to overcome those problems, a new extended Kalman filter (EKF) and a multi-failure diagnosis algorithm are developed to process both GNSS pseudorange and carrier phase measurements. We first introduce a new closed-loop EKF with partial ambiguity resolution as feedback to address the low accuracy issue. Then a multi-failure diagnosis algorithm is proposed to improve integrity and reliability. The core of this new algorithm includes using Carrier phase-based Receiver Autonomous Integrity Monitoring method for failure detection, and the double extended w test detectors to identify failure. A cooperative positioning experiment was carried out to validate the proposed method. The results show that the proposed closed-loop EKF can provide highly accurate positioning, and the multi-failure diagnosis method is effective in detecting and identifying failures for both code and carrier phase measurements.

Vision-Aided RAIM: A New Method for GPS Integrity Monitoring in Approach and Landing Phase.

In the 1980s, Global Positioning System (GPS) receiver autonomous integrity monitoring (RAIM) was proposed to provide the integrity of a navigation system by checking the consistency of GPS measurements. However, during the approach and landing phase of a flight path, where there is often low GPS visibility conditions, the performance of the existing RAIM method may not meet the stringent aviation requirements for availability and integrity due to insufficient observations. To solve this problem, a new RAIM method, named vision-aided RAIM (VA-RAIM), is proposed for GPS integrity monitoring in the approach and landing phase. By introducing landmarks as pseudo-satellites, the VA-RAIM enriches the navigation observations to improve the performance of RAIM. In the method, a computer vision system photographs and matches these landmarks to obtain additional measurements for navigation. Nevertheless, the challenging issue is that such additional measurements may suffer from vision errors. To ensure the reliability of the vision measurements, a GPS-based calibration algorithm is presented to reduce the time-invariant part of the vision errors. Then, the calibrated vision measurements are integrated with the GPS observations for integrity monitoring. Simulation results show that the VA-RAIM outperforms the conventional RAIM with a higher level of availability and fault detection rate.

Bayesian Fault-Tolerant Position Estimator and Integrity Risk Bound for GNSS Navigation

The advent of multiple Global Navigation Satellite System (GNSS) constellations will result in a considerable increase in the number of satellites for positioning worldwide. This substantial improvement in measurement redundancy has the potential to radically advance receiver autonomous integrity monitoring (RAIM) performance. However, regardless of the number of satellites, the performance of existing RAIM methods is sensitive to the assumed prior probabilities of individual fault hypotheses. In this paper, a new method is developed using Bayes’ theorem to generate upper bounds on posterior probabilities of individual fault hypotheses given current user measurements. These bounds are used in a Bayesian fault-tolerant position estimator (FTE) that minimizes integrity risk. The detection test statistic is a measurement-based integrity risk bound, which is directly compared with a pre-specified risk requirement. The associated challenge of quantifying continuity risk is resolved using a bounding approach, which is also detailed in this work. The new Bayesian FTE method is shown to be more robust to uncertainty in prior probability of fault occurrence than existing RAIM methods.

Integrity monitoring of fixed ambiguity Precise Point Positioning (PPP) solutions

Traditional positioning methods, such as conventional Real Time Kinematic (cRTK) rely upon local reference networks to enable users to achieve high-accuracy positioning. The need for such relatively dense networks has significant cost implications. Precise Point Positioning (PPP) on the other hand is a positioning method capable of centimeter-level positioning without the need for such local networks, hence providing significant cost benefits especially in remote areas. This paper presents the state-of-the-art PPP method using both GPS and GLONASS measurements to estimate the float position solution before attempting to resolve GPS integer ambiguities. Integrity monitoring is carried out using the Imperial College Carrier-phase Receiver Autonomous Integrity Monitoring method. A new method to detect and exclude GPS base-satellite failures is developed. A base-satellite is a satellite whose measurements are differenced from other satellite’s measurements when using between-satellite-differenced measurements to estimate position. The failure detection and exclusion methods are tested using static GNSS data recorded by International GNSS Service stations both in static and dynamic processing modes. The results show that failure detection can be achieved in all cases tested and failure exclusion can be achieved for static cases. In the kinematic processing cases, failure exclusion is more difficult because the higher noise in the measurement residuals increases the difficulty to distinguish between failures associated with the base-satellite and other satellites.

High accuracy tightly-coupled integrity monitoring algorithm for map-matching

A map-matching algorithm employs data from Global Positioning System (GPS), a Geographic Information System (GIS)-based road map and other sensors to first identify the correct link on which a vehicle travels and then to determine the physical location of the vehicle on the link. Due to uncertainties associated with the raw measurements from GPS/other sensors, the road map and the related methods, it is essential to monitor the integrity of map-matching results, especially for safety and mission-critical intelligent transport systems such as positioning and navigation of autonomous and semi-autonomous vehicles. Current integrity methods for map-matching are inadequate and unreliable as they fail to satisfy the integrity requirement due mainly to incorrect treatment of all the related uncertainties simultaneously. The aim of this paper is therefore to develop a new tightly-coupled integrity monitoring method for map-matching by properly treating the uncertainties from all sources concurrently. In this method, the raw measurements from GPS, low-cost Dead-Reckoning (DR) sensors and Digital Elevation Model (DEM) are first integrated using an extended Kalman Filter to continuously obtain better position fixes. A weight-based topological map-matching process is then developed to map-match position fixes onto the road map. The accuracy of the map-matching process is enhanced by employing a range of network features such as grade separation, traffic flow directions and the geometry of road link. The Receiver Autonomous Integrity Monitoring (RAIM) technique, which has been successfully applied to monitor the integrity of aircraft navigation, is modified and enhanced so as to apply it to monitor the quality of map-matching. In the enhanced RAIM method, two modifications are made: (1) a variable false alarm rate (as opposed to a constant false alarm rate) is considered to improve the fault detection performance in selecting the links, especially near junctions. (2) a sigma inflation for a non-Gaussian distribution of measurement noises is applied for the purpose of satisfying the integrity risk requirement.The implementation and validation of the enhanced RAIM method is accomplished by utilising the required navigation performance parameters (in terms of accuracy, integrity and availability) of safety and mission-critical intelligent transport systems. The required data were collected from Nottingham and central London. In terms of map-matching, the results suggest that the developed map-matching method is capable of identifying at least 97.7% of the links correctly in the case of frequent GPS outages. In terms of integrity, the enhanced RAIM method provides better the fault detection performance relative to the traditional RAIM.