ARAIM Protection Level Optimization Based on Feedback-Structure Subset Grouping

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.

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.

With the growing availability of navigation satellite constellations from more than one service provider, emergence of multi-constellation Global Navigation Satellite System (GNSS) makes it possible to develop Receiver Autonomous Integrity Monitoring (RAIM) capability with improved range measurement redundancy and accuracy. An emerging concept for receiver avionics that leverages the availability of multiple constellations, called Advanced RAIM (ARAIM), is being developed with a vision of not only providing worldwide approaches with vertical guidance, (Localizer Performance with Vertical Guidance, LPV-200, in particular) but it would also significantly expand Required Navigation Precision (RNP) operations availability worldwide. In the design of ARAIM, this improved capability stems mainly from introduction of an Integrity Support Message (ISM). The ISM is a periodically updated set of parameters that represent the statistical characterization of the core navigation constellation performance. This ISM is received by and utilized in the user avionics as a priori information in the ARAIM user algorithm. One critical issue is that when the statistical characterization of the core constellation performance is not accurate, ARAIM integrity performance may degrade. In our previous paper, we analyzed the sensitivity of Vertical ARAIM (V-ARAIM) performance to the ISM parameter deviations, leading to very interesting and important findings regarding ISM sensitivity. In our analysis, the term “deviation” is used to refer to an increase in the ISM values representative of actual core constellation performance relative to the broadcast ISM parameter values. In this paper, we extend the earlier analysis to Horizontal ARAIM (H-ARAIM). H-ARAIM requires a fault exclusion capability to meet the stricter RNP continuity requirements. The purpose of this paper is to see how the sensitivity characteristics of ARAIM to ISM deviation from actual values differ between H-ARAIM and V-ARAIM. The analysis in this paper, along with the analysis done previously for V-ARAIM, will help develop procedures that could be applied consistently and systematically to determine a set of “close-to-optimal” ISM parameter values to broadcast. This will facilitate global acceptance of an ISM update process.

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.

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.

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.

Advanced Receiver Autonomous Integrity Monitoring, or ARAIM, is a methodology for using redundant satellite range measurements to detect and exclude faults in one or multiple satellites or GNSS constellations while calculating position-domain bounds (“protection levels”) on the unknown errors generated by the remaining measurements. In [1], the authors of this paper demonstrated the ability of ARAIM, to support high-integrity applications of military users such as aircraft approaches to LPV minima (i.e., decision heights as low as 250 ft). This was done by modifying the civil approach to ARAIM described in [2, 3] to support military use of GPS M-code on the L1 and L2 frequencies along with the optional use of Galileo Open Service (OS) signals as a cross-check. Paper [1] examined military ARAIM performance for assumed levels of the key Integrity Status Message (ISM) parameters URA (bound on signal-in-space user range error), P_sat and P_const (per-satellite and per-constellation fault probabilities), and MFD (mean fault duration before a system alert is received) as a function of ground monitoring response time, defined as follows: 1) Online: updates ISM parameters (if needed) from once per hour to once per day. 2) Offline: updates ISM parameters (if needed) from once per week to once per quarter (three months). 3) Frozen: updates ISM parameters very infrequently, if ever (e.g., if URA or Psat becomes unexpectedly large). This paper examines several algorithms for ground monitoring in support of military ARAIM and generates achievable values of URA for these ISM update alternatives and a range of potential choices of P_sat and P_const. As with ARAIM itself, all of these algorithms are evaluated using the WAAS version of the Matlab Algorithm Availability Simulation Tool (MAAST) software toolset [4]. A hypothetical global network of 32 ground monitor reference stations has been created, starting with the existing 6 U.S. Air Force and 10 U.S. National Geospatial Agency (NGA) GPS monitor stations [5]. Ground monitoring simulations are conducted for this network and subsets of 24 and 16 stations from this network. The baseline ground monitor algorithm generates estimation and monitoring of satellite clock and ephemeris errors in a manner similar to that used by future multi-frequency SBAS to generate Dual Frequency Range Error (DFRE) bounds (analogous to User Differential Range Error, or UDRE, for single-frequency SBAS) [6]. This process is “snapshot”, meaning that satellite error estimates and DFRE bounds are generated independently at each ground monitor epoch. The minimum detectable range-domain errors that determine DFRE can be lowered (at the expense of timely responsiveness) by estimating the satellite errors over time as suggested in [7]. To apply this concept to the SBAS approach, a batch filter of varying duration (based on the desired ISM update rate) is implemented for comparison. Since the most rapid possible ISM update interval is assumed to be 15 minutes, even the snapshot approach takes credit for simple estimate error averaging within that interval, as ground monitor estimate updates (“epochs”) can be generated once per minute. Nominal results for these methods are presented in terms of achievable ARAIM URAs as a function on response time (after filtering, if applied) as a function of the values of P_sat and P_const to which the URA bound must apply. In these results, slower update rates correspond to lower URAs because of the additional averaging time (or greater number of separate monitoring epochs) that is allowed. However, the penalty is sluggish or perhaps no response to variations in satellite error behavior. To address this, Monte Carlo simulations of abnormal satellite error behavior are generated in which errors on one or multiple GPS satellites begin to grow unexpectedly. Online ground monitoring is able to rapidly update the ISM in response to these changes, but frozen ground monitoring cannot do so. This means that the URAs provided by a frozen (or offline) ISM must include substantial margin to allow for this possibility. As in [1], the URAs and corresponding P_sat and P_const values will be included in military ARAIM simulations of LPV aircraft approaches using the ARAIM (user) version of MAAST. The results will be used to recommend a specific ground monitor architecture to future military ARAIM designers as a function of the achievable ISM update rate.

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

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.

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%.

The precise point positioning (PPP)-based real-time-kinematic (RTK) method attracts increasing attention from both academia and industry because of its potential for high accuracy positioning with a shorter convergence time compared to the traditional PPP. Besides high accuracy, integrity monitoring (IM) is indispensable for safety–critical real-time land vehicle and aviation applications. As the traditional advanced receiver autonomous integrity monitoring (ARAIM) method is designed for (smoothed) pseudorange-based positioning, the complexity of multi-frequency multi-constellation PPP-RTK using carrier phase measurements has not been given sufficient consideration. This study proposes an IM scheme for multi-frequency multi-constellation uncombined PPP-RTK applying the ARAIM theory, with a new comprehensive threat model to accommodate not only pseudorange measurements, but also carrier phase measurements, and other fault events arising from the network corrections that support PPP-RTK. Characteristics of different types of faults are analyzed with the aid of numerical experiments. In addition, the impact of ambiguity-fixed solutions on PPP-RTK integrity performance is investigated. The authors have also conducted case studies, including static and real-kinematic positioning experiments. Experiments have demonstrated that fast convergence in accuracy and the position error bounds, or protection levels, with a given integrity risk, in horizontal position components of PPP-RTK could be achieved. For the open sky environments on a highway, the protection levels estimated by PPP-RTK solutions have the potential to meet the alert limit requirement for road transportation using ambiguity-fixed PPP-RTK positioning under the assumption that the risks of wrong ambiguity fixing are very small and can be ignored.

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.

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.