A Modified Range Consensus Algorithm Based on GA for Receiver Autonomous Integrity Monitoring

The field of GNSS integrity comprises a plethora of integrity related algorithms, each featured with different characteristics. These algorithms use a priori information of some parameters in order to provide integrity information to the user. One type of these algorithms, RAIM, is extensively used in the maritime domain in order to provide an indication if the position calculated is likely to be outside of the requirements of maritime performance standards, as it is required by IMO regulation. The term “Classical RAIM” refers to fully autonomous algorithms which can implement only one, a combination or all the following capabilities: Fault Detection, Fault Exclusion and Protection Level computation. In order to provide this indication, a GNSS receiver shall incorporate RAIM using at least Fault Detection (FD) algorithms, or similar means to determine if accuracy is within the performance standards and provide an integrity indication. In order to harmonise and ensure the correct RAIM algorithms implementation, its usage is standardised by the standard IEC 61108, in particular 1 and 3 series for GPS and Galileo constellations. The current RAIM definition in standards does not constrain the specific RAIM algorithm implementation deliberately in order to let the manufactures implement the most suitable one for their applications. In addition, in order to prove that a receiver is compliant with the requirements specified in this standard, two simple tests are proposed. The first one intends to test the performance of the receiver under safe and caution states, and the other one tests the unsafe state. However, as it is recognised by IALA in the last workshop on the future of marine radiobeacons, “current implementations of Receiver Autonomous Integrity Monitoring (RAIM) are considered inadequate due to the current lack of maritime specific RAIM algorithms.” It has been observed some limitation of current RAIM implementations beyond a specific algorithm for maritime. First of all, the relaxed requirements, in terms of high probabilities of false alarms and of not detecting an error condition, might be justified due to the limitations expected from some implementations of RAIM algorithms. In addition, requirements for GPS and Galileo RAIM differs significantly. Secondly, they are based on some hypothesis as single-failure assumption, a priory estimation of error values components (too conservative in order to provide the integrity required for any user under any condition) or the no existence of biases in faultfree measurements that may not ensure the integrity required if these hypothesis are not met in maritime domain. Finally, considering the simplicity of the aforementioned tests, which do not provides a set of parameters for testing or even assess the probabilities of miss-detection or false alarm, there is no assurance that the RAIM algorithm implemented in the receiver correctly provides the integrity required. To ensure this, it would be necessary to improve the test method in order to characterize the value of the introduced error and add new tests to evaluate the complete set of requirements to the implemented integrity algorithm. The GSA (European GNSS Agency), under GSALOT3TRANS framework contract, analysed the implementation of EGNSS in the area of transport applications. In particular, the Specific Contract 7 (SC7) MarTech it is being assessed the potential use of SBAS combined with RAIM in maritime and inland waterways (IWW) domains. The industrial partners of this specific contract are VVA Brussels (prime) and GMV. Although is not contemplated in any of the aforementioned regulations, the use of SBAS on top of other GNSS technologies that use RAIM may imply some important advantages. The first advantage and the most obvious of the SBAS corrections usage on top of RAIM is the increased performances in terms of accuracy, availability and continuity. The use of SBAS integrity information may also overcome the limitation of some RAIM algorithms that are only able to detect one faulty satellite. SBAS integrity messages also contains valuable information about the estimation of error components that may be used by the RAIM algorithm on real time with enough level of integrity to improve the performance. The aim of this paper is to provide a summary of the activities developed so far as part of GSALOT3TRANS SC7 regarding the current RAIM limitations in the maritime domain and the potential use of SBAS in combination with it to overcome some of these limitations.

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

The space constellation of BeiDou Navigation Satellite System(BDS)has three main components, Geostationary Earth Orbits (GEOs), Medium Earth Orbits (MEOs) and Inclined Geosynchronous Satellite Orbits(IGSOs).This paper selected 6 satellite respectively in three types to simulate their exiting service, and used the statistical methods to assess receiver autonomous integrity monitoring（RAIM） availability and fault detection （FD） capability of BeiDou14（Phase Ⅱ with 14 satellites）under the circumstances. This paper assessed RAIM availability performance from satellites and constellation geometry configuration by the number of visible satellites (NVS, NVSg5) and position dilution of precision (PDOP, PDOP≤6) together. The FD capability of RAIM is assessed by the maximum minimal detectable bias (MDB) and the maximum minimal detectable effect (MDE). The analyses of simulation results testify that the exiting of single MEO or IGSO satellite have no obvious effect on RAIM availability and error detection ability. However GEO satellite’s exiting can make the number of points in where the constellation geometry is not available and maximum minimum detectable deviation and maximum minimum detectable influence significantly increase. Relative to other two satellites, GEO satellites’ health have a significant impact on the RAIM performance of BDS.

At the current stage, the service performance analysis of BeiDou satellite navigation system (BDS) can provide important references for BDS applications. Accuracy, integrity, availability, and continuity are important indicators of the service capability of the satellite navigation system. In this paper, the relationships among them are analyzed firstly. Secondly, for describing accuracy, position dilution of precision (PDOP) is selected and the quantitative calculation formulas of its availability and continuity are established. Then PDOP of BDS in Asia-Pacific region is simulated and analyzed, the results show that the average availability and continuity of PDOP in the service area during 24 h are up to 99.60% and 0.9947/h, respectively. Thirdly, this paper has a brief introduction to weighted least square algorithm of receiver autonomous integrity monitoring (RAIM) and analyzes the relationships among the probability of false alert, the probability of missed detection, threshold, the minimum detectable error and the number of visible satellites. The threshold and the minimum detectable error will increase with the increasing number of visible satellites when given the false alert probability and missed detection probability, respectively. Finally, the quantitative calculation formula of RAIM availability and continuity is presented and RAIM availability and continuity are simulated and analyzed as well, the results show that the average availability and continuity in the service area are up to 99.95% and 0.9984/h, respectively.

Satellite Navigation Receiver Autonomous Integrity Monitoring (RAIM) is developed for detection and recognition of satellite faults using the redundant observation values of receiver itself. This article first makes a comparative analysis to the conventional and recently proposed RAIM fault detection and identification methods, and then points out that the conventional fault identification methods have such weakness as low fault detection probability and high false alarm probability in the situation of simultaneous multiple faulty satellites, while the recently proposed multi-satellite fault identification methods require excessive computation work though enjoy a higher detection probability. To solve these problems, a new RAIM multiple satellite fault detection and recognition method is proposed in this paper, which is characterized by high detection probability, low false alarm probability and low computation load, practicable for project implementation. Based on a simulation analysis, it turns out that this method has a higher detection probability without false alarm at all. This method requires so low computation work that it only needs 2 m times of least squares residuals fault detection and m times of fault identification in m (m > 0) faulty satellites situation, showing a very promising application value.

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.

According to the measurement noise feature of GPS receiver and the degeneracy phenomenon of particle filter (PF), in order to alleviate the sample impoverishment problem for PF, GPS receiver autonomous integrity monitoring (RAIM) algorithm based on PF algorithm combining neural network was proposed, which was used to improve the importance state adjustment of particle filter algorithm. The PF algorithm based on neural network is analized. And the test statistic of satellite fault detection is set up. The satellite fault detection is undertaken by checking the cumulative log-likelihood ratio (LLR) of system state of GPS receiver.The proposed algorithm was Validated by the measured real raw data from GPS receiver, which are deliberately contaminated with the bias fault and ramp fault, the simulation results demonstrate that the proposed algorithm can accurately estimate the state of GPS receiver in the case of non-Gaussian measurement noise, effectively detect and isolate fault satellite by establishing log-likelihood ratio statistic for consistency test and improve the accuracy of detection performance.

Receiver Autonomous Integrity Monitoring (RAIM) has been successfully used for fault detection and exclusion in aviation applications. For airborne environment, the Geometric Dilution Of Precision (GDOP) and pseudo-range measurement quality is good. However, for urban canyon environment the GDOP may increase much and the pseudo-range measurement may contain huge multipath error. In this case, the exclusion of any pseudo-range measurement with multipath may make the GDOP increase much more and make the user position accuracy even worse. In this work, we propose an Urban Adapted-RAIM (UA-RAIM) algorithm to adjust the particular poor satellite geometry and multipath environment of urban canyon. Exclusion Accuracy Filter (EAF) is designed to check the position accuracy before and after fault exclusion. The fault satellite which can improve the positioning accuracy is processed using weighted estimation technique. Static experiment was carried out and a reflector was set to simulate the urban canyon environment. The user position errors in North, East and Up direction are 63.70%, 55.25% and 64.52% lower compared with the RAIM algorithm. From the urban vehicle tests, the vehicle positions of UA-RAIM are more like a straight line compared with RAIM. The 95% accuracy decreases from 20.32m to 16.55m using UA-RAIM and about 81.45% compared with the original.

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.

As an advanced algorithm for receiver autonomous integrity monitoring (RAIM), advanced RAIM (ARAIM) has gained considerable attention in the civil aviation sector and is gradually finding applications in other fields. However, with the increasing number of visible satellites, the number of fault subsets processed by the multiple hypothesis solution separation (MHSS) method grows exponentially, imposing a substantial computational burden on the receiver. Furthermore, ARAIM’s uniform distribution of integrity and continuity risks among fault subsets results in overly conservative protection levels (PLs). These challenges are often addressed as separate issues. However, this study proposes a novel PL optimization algorithm that incorporates a subset grouping method with a feedback structure to reduce the number of fault subsets, thereby decreasing detection time. In addition, an improved cuckoo search algorithm (ICSA) is developed to allocate integrity and continuity risks more effectively, optimizing the PLs. Experimental results demonstrate the effectiveness of the proposed method. Compared to ARAIM, without IMU, the protection level optimization of proposed algorithm improves by 34.38% and 35.06% in the vertical and horizontal directions, respectively; with IMU, the protection level optimization of proposed algorithm improves by 74.21% and 74.49% in the vertical and horizontal directions, respectively. In addition, due to the fault subsets reduction, the fault detection time is reduced by 54%, 47%, and 26% compared with ARAIM, FSPA, and Feedback ARAIM, respectively.

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as Autonomous ground vehicles (AGVs) and autonomous aerial vehicles (AAVs). The visual odometry-based method can provide accurate positioning in a short period but is subject to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation, which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite system (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected by AAV and AGV in indoor and outdoor environments, respectively. The open-source code is available at https://github.com/ZHENGXi-git/SafetyQuantifiable-PLVINS

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.

Railway operation is based on various safety critical control and signalling operations which are conventionally realized by stationary infrastructure. Acquisition, maintenance and operation of these components are generally very cost-intense and compromise the future of railways lines with low traffic volumes. These so called secondary railway lines are essential to satisfy mobility requirements especially in rural regions and differ from conventional main routes as they provide several operational characteristics (e.g. single-laned) which enables simplified, decentralized administration of route setting and train movements. In the work presented, a general overview of a vehicle-mounted safety logic concept which includes a detailed description of the essential probabilistic multisensor GNSS based train position estimation module is suggested. The localization and tracking is performed by adopting an Extended Kalman Filter (EKF). Additionally, a Fault Detection (FD) Receiver Autonomous Integrity Monitoring (RAIM) for safety critical applications is used to ensure integrity of the estimated positions. The GNSS parts of the developed localization and integrity module are validated using a real world Software Defined Radio (SDR) recorded GNSS-RF-dataset which is played back in a laboratory environment enabling reproducable validation cases with different GNSS receivers and algorithm parametrizations.

The satellite transmits carrier signal to Global Positioning System (GPS) receivers suffered signal degradation in varied environmental conditions, from space through atmosphere to ground, especially due to the effects of multipath fading. In order to investigate the error performance of GPS signal transmission over multipath fading situations, a vector delay-locked tracking loop structure is applied to GPS receiver with the support of the Kalman filtering algorithms in this paper. The vector delay-locked tracking loop system is supported with receiver autonomous integrity monitoring (RAIM) and fault detection and exclusion (FDE) algorithm to prevent signal unlocked situation. Simulations of the satellite signal to GPS receiver over the multipath fading noise are investigated under three channel conditions: the additive Gaussian white noise (AWGN), Rayleigh fading noise, and Rician fading noise. The extended Kalman filter (EKF) algorithm is applied to improve the system performance. Analysis for various combinations of the above mentioned algorithms is studied and compared.

In this article, the problem of detecting and excluding faulty global navigation satellite systems (GNSSs) measurements at the receiver end is formulated as a parameterized quadratic programming (PQP) problem. Compared to the existing fault detection and exclusion (FDE) methods, which mostly rely on exhaustive search, the PQP method is computationally efficient for finding the outliers even when the number of outliers is moderate or large. In the context of multiconstellation GNSS where the probability of multiple simultaneous faults is increased, the PQP method is ideal for the task of fault exclusion since the computation time does not increase with the number of fault hypotheses. It is noted that this article addresses the computational load due to the exclusion function only. The integrity risk bound and continuity risk bound are of fundamental importance to assess the performance of FDE algorithms for safety-critical applications. With the aim to benefit safety-critical applications, the PQP method is integrated with the integrity risk bound and the continuity risk bound derived for FDE using Chi-squared receiver autonomous integrity monitoring (RAIM). It is emphasized that the integration of the PQP method and the integrity and continuity risk bounds do not make the PQP method a practical integrity monitoring method. This is because the computation of the integrity risk bound is still combinatorial and the resulting integrity risk bound is rather conversative. Instead, the integration allows for the opportunity to refine the PQP method so that it can be considered a practical integrity monitoring method. In particular, improvements to reduce the computational load for the Chi-squared RAIM FDE integrity risk bound calculation can readily be applied to the integrity risk bound calculation for the PQP method. Also, further research on PQP parameter tuning can be pursued to tighten the integrity risk bound.