Horizontal Advanced RAIM Performance Sensitivity to Mischaracterizations in Integrity Support Message Values

As Global Navigation Satellite System continues to upgrade, the number of satellite increases and new signals in different frequencies are available. This offers users some benefits such as increased availability and improved diversity of signal. For safety-of-life users like aviation, higher availability of integrity is the key goal. A technology providing integrity with little or no ground infrastructure is receiver autonomous integrity monitoring (RAIM). As more satellites are visible to the receiver, the redundancies increase and RAIM availability for detecting faults improves. Moreover, the ionosphere delay could be eliminated through multi-frequency combination. The Advanced RAIM (ARAIM) uses multi-constellation and multi-frequency GNSS and hopes to provide vertical guidance for aircraft. For evaluating ARAIM in flight, an inexpensive flight is executed by an unmanned aircraft systems (UAS) with a developed ARAIM software receiver. The receiver supports civilian signals including GPS L1 C/A, L2C and GLONASS G1, G2. An efficient software architecture are proposed for the real-time purpose. Using the outputs from the receiver, a developed ARAIM toolbox computes the protection levels. Field test is conducted by flying the UAS on a live test campaign. The ARAIM performance during the flight shows that the vertical protection level achieves the target.

The Advanced Receiver Autonomous Integrity Monitoring (ARAIM) provides an Aircraft‐Based Augmentation System function for aircraft Global Navigation Satellite System equipment. Currently, the consideration of temporal correlations of test statistics is an important update to the ARAIM baseline algorithm. However, due to the tight budgets of integrity and continuity, the updated ARAIM Fault Detection and Exclusion (FDE) is not satisfactory in availability coverage. The baseline algorithm is based on equal allocation of integrity and continuity, which causes ARAIM to be susceptible to faults and satellite outages. An availability optimisation of ARAIM FDE based on dynamic budget allocation is proposed, aiming to make full use of the prior knowledge provided by the Integrity Support Message (ISM). The contribution of each solution separation test in the ARAIM FDE to the total continuity and integrity risks is estimated, and the continuity and integrity allocation constraints are obtained. The impacts of faults and satellite outages on availability are analysed, and a predictive Protection Level (PL) for ARAIM FDE is proposed for use as an objective function for optimising risk allocation. Then, a risk allocation method based on the multiplier penalty function method is proposed. The performances of the baseline algorithm and the proposed method under the maximum Number of Effective Samples are simulated. For dual‐constellation H‐ARAIM, the optimised Horizontal PL (HPL) is 40% lower than that of the baseline algorithm on average, and the average number of critical satellites supporting RNP 0.3 is less than 1 worldwide. For the three‐constellation V‐ARIAM, the optimised HPL is 27% lower than that of the baseline algorithm on average, and the optimised Vertical PL (VPL) is 18% lower. The proposed method has high availability coverage and can better respond to the requirements for high‐performance navigation services.

Global navigation satellite system (GNSS) plays a crucial role in many fields, such as aerospace and transportation. Integrity is the measure of trust used in GNSS positioning especially in safety-critical applications. Advanced receiver autonomous integrity monitoring (ARAIM), taking full advantage of multi-constellation GNSS, shows huge potential to provide vertical navigation in civil aviation en route navigation and terminal approaches. However, the multi-constellation ARAIM also greatly exposes computational complexity and potential performance hazards in fault modes determination and fault-tolerant positioning. From the perspective of integrity risk control, rather than the pursuit of better positioning accuracy blindly for safety-critical applications, the concept of constellation dynamic selection is proposed and implemented in ARAIM and the performance analysis is discussed in this paper. Only the best two constellations which have the best vertical geometry performance are involved in ARAIM calculation anytime anywhere. The proposed method shows superiority in both integrity availability and computational complexity in both simulations and actual GNSS signal experiments. While the computational complexity is less than 10% of that using four constellations, 100% availability under LPV-200 criteria can be achieved in worldwide coverage experiment. The proposed method also overcomes the shortcomings of ARAIM with two fixed constellations and shows good robustness under depleted scenarios. Furthermore, the statistics results from observation stations proved the applicability and generality of the proposed method under current developing GNSS constellations.

Two Receiver Autonomous Integrity Monitoring (RAIM) architectures, Advanced RAIM (A-RAIM) and Relative RAIM (R-RAIM), are compared with two different RAIM algorithms, the Classic method and the Multiple Hypothesis Solution Separation (MHSS) method. The difference between A-RAIM and R-RAIM is in the positioning methods that produce different error models and projection matrices for integrity monitoring. The difference between RAIM algorithms lies in the methods of risk distribution. The influences of different positioning methods on integrity results are analysed in this paper via a generalized RAIM framework. Simulation results for the LPV-200 service with worldwide coverage show that the R-RAIM position domain method has the best results, while the differences between these methods decrease with application of the optimization method.

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.

Signal-In-Space User Range Errors (SIS UREs) are assumed to be overbounded by a normal distribution with a standard deviation represented by the User Range Accuracy (URA). The BeiDou Navigation Satellite System (BDS) broadcast URA is not compatible with the historical SIS URE performance that affects the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) False Alert Probability (Pfa) and availability evaluation. This study compares the BDS broadcast and precise ephemeris from 1 March 2013 to 1 March 2017 to obtain SIS UREs. Through analyzing the statistical characteristics of the SIS UREs, we obtain the standard deviation σURE for the accuracy and continuity and σURA used for the integrity of the SIS UREs. The results show that the broadcast σURA of 2 m cannot completely overbound SIS UREs for all BDS satellites, but the σURA of 2.4 m can. Then, we use the σURA of 2.4 m to evaluate the ARAIM Pfa and availability. The results show that the Pfa may increase to 2 × 10−5 and exceed its limit by an order of magnitude. We also consider the differences between the SIS UREs of Geostationary Earth Orbit (GEO), Inclined Geo-Synchronous Orbit (IGSO), and Medium Earth Orbit (MEO). The results indicate that all Pfa values calculated by the computed σURE are less than the Pfa in the Integrity Support Message (ISM) for the worst-performing GEO satellite. The approximately 55% Pfa calculated by the computed σURE is less than the Pfa in ISM for the worst-performing IGSO satellite. Most Pfa values calculated by the computed σURE is less than the Pfa in the ISM for the worst-performing MEO satellite. For BDS satellites, the Pfa is mainly affected by σURE. When the σURA of 2.4 m is used to evaluate the availability, the computed availability is lower than the availability calculated by the broadcast σURA/σURE and the greatest degradation can reach 25%.

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.

This paper describes a process by which Global Navigation Satellite System (GNSS) nominal statistics and fault rates can be assessed with high confidence. The use of a GNSS in Advanced Receiver Autonomous Integrity Monitoring (ARAIM) requires knowledge of the level of performance of the signal-in-space (SIS) user range error (URE) in both nominal and faulty conditions. The performance characterization is done through a careful analysis of historical data, from which a determination of whether or not it meets the performance commitments of the constellation service provider (CSP) can be made. Typically, this analysis is carried out though a comparison of the broadcast navigation messages with post-processed precise estimates of the satellite clock and orbit states. This paper highlights the issues with such an approach as well as mitigation strategies to those issues. The issues primarily impact the estimates of fault rates rather than nominal statistics, as the number of faults is small compared to the overall amount of nominal data. In particular, methods of ensuring integrity in the logged broadcast navigation messages and precise clock and orbit products used in the analysis are described. In order to verify precise clock and orbit products, a Kalman filter (KF) to produce independent GNSS clock bias estimates has been developed and tested. The KF leverages the International GNSS Service (IGS) receiver network to produce estimates of GNSS clock biases given precisely known positions of the receivers as well as precise satellite orbit products. The KF serves multiple purposes in detecting and verifying faults. First, when only low rate (5 or 15 minute) clock products are available, higher rate products can be produced to detect short faults. Second, when precise clock products are available, the KF serves as a secondary check to protect against erroneous external precise products, which have been observed. Initial testing of the filter has shown an RMS error of under 20 centimeters for a five day run of GPS clock bias estimates, which is sufficient performance for precise product verification and fault detection.

Civil aviation transportation equipment is more convenient and faster than other transportation tools and is an essential part of intelligent transportation. It is significant to study the reliability of positioning information and enhance traffic safety. Advanced receiver autonomous integrity monitoring (ARAIM) can provide vertical guidance during the different navigation stages in civil aviation fields. The traditional multiple hypothesis solution separation (MHSS) algorithm distributes the probability of hazardous misleading information (PHMI) and probability of false alarm (PFA) uniformly over all visible satellites resulting in reduced global availability of ARAIM. Aiming at this problem, we proposed an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to redistribute integrity and continuity risks and establish a protection level optimization model. Based on the real BeiDou navigation satellite system/global positioning system (BDS/GPS) data, the experimental results show that the optimized algorithm can reduce the vertical protection level (VPL), and the ARAIM global availability of BDS/GPS is improved by 1.73%∼2.73%. The optimized algorithm can improve the availability of integrity monitoring at different stages of the navigation system and provide a basis for ensuring the reliability of the positioning results.

Nowadays one can use four global navigation satellite systems (GNSS). Two of them are complete constellations (GPS, Glonass) and two (Beidou, Galileo) are already usable and will be finish in the near future. Additionally satellite based augmentation systems (SBAS) like WAAS, EGNOS, GAGAN or QZSS complement the GNSS service. However, within all systems one can observe changes, modifications, and updates every year. This can be related to satellite renewables leading to signal property changes. Especially, for safety critical applications using GNSS, like advanced receiver autonomous integrity monitoring (ARAIM) or ground-based augmentation systems (GBAS) the new or changed signal properties are of high interest. With the help of detailed information about the signal deformation and the received signal power it is possible to calculate realistic error bounds and consequently realistic protection level for these kinds of safety critical applications. This paper presents an overview of the findings according new signals or signal configurations of GPS, Beidou and QZSS of the last two years. After a brief introduction of the measurement facility the paper will introduce basic analysis about the quality of the signal shape in spectral and modulation domain. Using our precise calibrated measurement facility, we will also present an analysis of the transmitted satellite signal power including estimates about the power sharing among individual signal components within each band. Considering the measured power in relation to the boresight angle of the satellite one can derive a cut through the antenna pattern of the satellite and can assess the antenna symmetry properties. Examples for different satellites will be presented. Finally, we will end with a conclusion regarding the considered signal developments and its impact on GNSS users.

The integrity analysis of systems like Satellite-Based Augmentation Systems (SBAS), Ground-based Augmentation Systems (SBAS), Receiver Autonomous Integrity Monitoring (RAIM), or Advanced RAIM (ARAIM) requires proving that the actual distribution of errors can be replaced by a simpler distribution, often a gaussian distribution. Two key results have been used to generate these overbounding distributions: cdf bounding and paired overbounding. Although these two results are very powerful, each of them has a weakness that limits their direct application. The goal of this paper is to overcome some of these weaknesses. We will first present a method to determine gaussian overbounding distributions that combines cdf bounding and paired overbounding. This method is based on the determination of an intermediate overbounding distribution that is symmetric and unimodal. Then, we present a modification of the paired overbounding theorem that relaxes the cdf paired overbounding requirement mentioned above. These techniques are the basis of a MATLAB toolset that computes rigorous gaussian overbounding distributions for any sample distribution.

In this paper, we develop a high-integrity and high-accuracy navigation method for safety-critical uncrewed aircraft systems (UAS) surface operations. The method processes multi-constellation carrier phase differential (CPD) global navigation satellite system (GNSS) signals in a horizontal advanced receiver autonomous integrity monitoring (H-ARAIM) algorithm. The objective is to address specific UAS surface navigation challenges, including narrow taxiways/taxilanes requiring high accuracy and the need for continuous localization. Additionally, proximity to structures causes satellite signal obstructions and large multipath errors. The short-baseline differential approach (<5 km) eliminates common-mode GNSS errors including ionospheric delay, tropospheric delay, and satellite clock and orbit errors. Integrity monitoring is achieved through fault detection and risk bounding using a solution separation (SS) method. To enable rapid convergence of position solutions, we integrate geometry-free filtering (GFF) of dual-frequency measurements to provide improved cycle ambiguity estimates prior to reaching the communication volume where differential corrections can be applied. Additionally, we develop a refined variance model for GFF that accounts for UAS experiencing different multipath environments throughout its mission. Performance is evaluated using both snapshot and batch processing approaches with GPS and Galileo measurements at three geographically diverse locations: Chicago O'Hare (Illinois), Fairbanks (Alaska), and Hilo (Hawaii). Simulation results suggest that the proposed architecture achieves 100% availability against the 1.5 m horizontal alert limit (HAL) across all scenarios.

Integrity monitoring (IM) is essential if GNSS positioning technologies are to be fully trusted by future intelligent transport systems. A tighter and conservative stochastic model can shrink protection levels in the position domain and therefore enhance the user-level integrity. In this study, the stochastic models for vehicle-based GNSS positioning are refined in three respects: (1) Gaussian bounds of precise orbit and clock error products from the International GNSS Service are used; (2) a variable standard deviation to characterize the residual tropospheric delay after model correction is adopted; and (3) an elevation-dependent model describing the receiver-related errors is adaptively refined using least-squares variance component estimation. The refined stochastic models are used for positioning and IM under the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) framework, which is considered the basis for multi-constellation GNSS navigation to support air navigation in the future. These refinements are assessed via global simulations and real data experiments. Different schemes are designed and tested to evaluate the corresponding enhancements on ARAIM availability for both aviation and ground vehicle-based positioning applications.

Limited sensitivity analysis of ARAIM availability for LPV-200 over Australia using real data

This thesis implements a state-of-the-art solution separation advanced RAIM (ARAIM) algorithm as it is written as reported in the literature. Specifically, a GNSS fault detection and exclusion algorithm for a multi-constellation GNSS was implemented in software and tested against simulated data. RAIM algorithms have been created in many forms over the last couple of decades and are still in development today. The position solution results produced by this ARAIM algorithm were compared to that of a snapshot weighted least squares (WLS) solution in which failed satellites are removed before processing and an WLS solution with no corrections applied. In addition, the difference in position solution between ARAIM and the simulation truth was compared to the ARAIM reported horizontal and vertical protection limits, as well as, the position performance criteria. This thesis also investigates the performance of the exclusion method and how it affects the performance of the overall ARAIM algorithm. The algorithm implemented and tested in this thesis will be used as a basis of comparison for on-going research into robust GNSS processing techniques.