Autonomous Integrity Research Articles

RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults.

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement. For any fault scenario affecting h measurements, the eigenvalue problem that defines the worst-case fault is expressed and analyzed in terms of these orthogonal subspaces, which enables further analysis. For h>(m-n), where n is the number of estimated variables, it is known that there always exist faults that are undetectable from the residual vector, yielding an infinite value for the failure mode slope. This article uses the range space and its complement to explain: (1) why, for fixed h and n, the failure mode slope decreases with m; (2) why, for a fixed n and m, the failure mode slope increases toward infinity as h increases; (3) why a failure mode slope can become infinite for h≤(m-n). A set of examples demonstrate the results of the paper.

Opportunistic Autonomous Integrity Monitoring for Enhanced UAV Safety

An opportunistic advanced receiver autonomous integrity monitoring (OARAIM) algorithm for unmanned aerial vehicle (UAV) navigation is developed. This algorithm fuses ambient terrestrial signals of opportunity (SOPs) with global navigation satellite system (GNSS) signals to improve the capability of detecting GNSS and SOP measurement faults and provide tight protection level (PL) bounds. A receiver is assumed to make pseudorange measurements on multiple GNSS satellites and terrestrial SOPs. Experimental tests are presented injecting simulated faults into the OARAIM algorithm, which successfully detects the faults in GPS satellites and SOPs. Moreover, the OARAIM algorithm and the inclusion of the SOPs reduces the vertical and horizontal PLs by more than 55% and 70%, respectively, compared to only using GNSS measurements.

Integrity monitoring scheme for single-epoch GNSS PPP-RTK positioning

Integrity monitoring for precise point positioning is critical for safety-related applications. With the increasing demands of high-accuracy autonomous navigation for unmanned ground and aerial vehicles, the integrity monitoring method of high-precision positioning has become an essential requirement. While high precision Global Navigation Satellite Systems (GNSS) positioning is widely used in such applications, there are still many difficulties in the integrity monitoring method for the multi-frequency multi-GNSS undifferenced and uncombined Precise Point Positioning (PPP). The main difficulties are caused by using the measurements of multiple epochs in PPP. Based on the baseline Multiple Hypothesis Solution Separation (MHSS) Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm, this paper discusses the feasibility of the pseudorange-based baseline ARAIM method on the single-epoch PPP based on Real-Time Kinematic (RTK) networks (PPP-RTK) framework to overcome these difficulties. In addition, a new scheme is proposed to transfer the conventional PPP process into the single-epoch PPP-RTK framework. The simulation results using the proposed model are analyzed in this study. The Protection Levels (PLs) estimated by PPP Wide-lane Ambiguity Resolution (PPP-WAR) model with regional corrections can reach the meter level and the PLs estimated by PPP Ambiguity Resolution (PPP-AR) and PPP-RTK models are usually the sub-meter level. Given a horizontal Alert Limit (AL) of 1.5 m, the global coverage of availability above 99.9% for PPP-WAR, PPP-AR, and PPP-RTK can reach 92.6%, 99.4%, and 99.7% respectively. The results using real kinematic data also show that tight PLs can be achieved when the observation conditions are good.

T-RAIM Approaches: Testing with Galileo Measurements.

Several applications rely on time retrieved from Global Navigation Satellite System (GNSS), and this pushes for integrity tailored to timing. Integrity information could be broadcast by GNSS itself, but currently, there are no GNSSs providing such integrity information for a timing application. The integrity provided by GNSS itself could not be timely enough for real time users and does not include local effects due to multipath or other local interferences. In order to fill the gap, integrity can be locally/autonomously computed by the receiver using Timing Receiver Autonomous Integrity Monitoring (T-RAIM) algorithms. Three T-RAIM algorithms have been designed, implemented, and tested; specifically, the algorithms are Forward-Backward (FB), Danish, and Subset. The algorithms are applied to the classical Position Velocity and Timing (PVT) solution and to the time-only case assuming the receiver coordinates are known. Tests using two identical receivers located in different scenarios, open-sky and obstructed, have been carried out to validate the algorithms proposed. The increased redundancy obtained from the knowledge of the receiver coordinates play a fundamental role for the integrity algorithms performance. The benefits of the T-RAIM algorithms activation, in signal degraded conditions, clearly emerged in terms of frequency error and Allan Deviation (ADEV). A small increase of the execution time has been observed when the T-RAIM algorithms are used.

Integrity monitoring scheme for undifferenced and uncombined multi-frequency multi-constellation PPP-RTK

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.

UAV Sensors Autonomous Integrity Monitoring—SAIM

For Unmanned Aerial Vehicles (UAVs), it is of crucial importance to develop a technically advanced Collision Avoidance System (CAS). Such a system must necessarily consist of many sensors of various types, each one having special characteristics and performance. The poor performance of one of the sensors can lead to a total failure in collision avoidance if there is no provision for the performance of each separate sensor to be continuously monitored. In this work, a Sensor Autonomous Integrity Monitoring (SAIM) methodology is proposed. The configuration of the sensors and their interaction is based on a fusion procedure that involves a total of five sensors. Accordingly, the performance of each one of the sensors is continuously checked against the combined (fused) operation of the other four. A complementary experiment with a total of four sensors, one of which had low performance, was also conducted. Experimental results reveal a reliable approach for Sensor Autonomous Integrity Monitoring (SAIM). The method can be easily extended to a larger number of sensors.

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

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

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.

3D printed microfluidics: advances in strategies, integration, and applications.

The ability to construct multiplexed micro-systems for fluid regulation could substantially impact multiple fields, including chemistry, biology, biomedicine, tissue engineering, and soft robotics, among others. 3D printing is gaining traction as a compelling approach to fabricating microfluidic devices by providing unique capabilities, such as 1) rapid design iteration and prototyping, 2) the potential for automated manufacturing and alignment, 3) the incorporation of numerous classes of materials within a single platform, and 4) the integration of 3D microstructures with prefabricated devices, sensing arrays, and nonplanar substrates. However, to widely deploy 3D printed microfluidics at research and commercial scales, critical issues related to printing factors, device integration strategies, and incorporation of multiple functionalities require further development and optimization. In this review, we summarize important figures of merit of 3D printed microfluidics and inspect recent progress in the field, including ink properties, structural resolutions, and hierarchical levels of integration with functional platforms. Particularly, we highlight advances in microfluidic devices printed with thermosetting elastomers, printing methodologies with enhanced degrees of automation and resolution, and the direct printing of microfluidics on various 3D surfaces. The substantial progress in the performance and multifunctionality of 3D printed microfluidics suggests a rapidly approaching era in which these versatile devices could be untethered from microfabrication facilities and created on demand by users in arbitrary settings with minimal prior training.

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

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

A Positioning System in an Urban Vertical Heterogeneous Network (VHetNet)

Global navigation satellite systems (GNSSs) are essential in providing localization and navigation services to most of the world due to their superior coverage. However, due to high pathloss and inevitable atmospheric effect, the positioning performance of any standalone GNSS is still poor in urban areas. To enhance the positioning performance of legacy GNSSs in urban areas, a positioning system, which utilizes high altitude platform station (HAPS) and 5G gNodeBs (gNBs), in a futuristic urban vertical heterogeneous network (VHetNet) is proposed. In this paper, we demonstrate the effectiveness of gNBs in improving the vertical positioning performance for both the GPS-only system and the HAPS-aided GPS system by analyzing the impact of the density of gNBs and the pseudorange error of gNB on the positioning performance of the gNB augmented positioning systems. We also demonstrate the effectiveness of receiver autonomous integrity monitoring (RAIM) algorithms on the HAPS and/or gNB aided GPS systems in urban areas.

A GNSS Instrumentation Covert Directional Spoofing Algorithm for UAV Equipped With Tightly-Coupled GNSS/IMU

Measurement and navigation users cannot operate without the positioning, navigation, and timing (PNT) service provided by the global navigation satellite system (GNSS) instrumentation, but users are also faced with unpredictable and complex interference and even spoofing. In order to effectively control and even counter the unmanned aerial vehicle (UAV) that may pose security threats, using the GNSS spoofing technology to counter UAV is an effective and important technical means. Compared with the existing spoofing algorithms, this article fully considers the navigation system configuration and flight control principle, as well as antispoofing module of UAV equipped, the UAV dynamic model under GNSS spoofing environment is established, and the response law of UAV state to spoofing is analyzed. In order to realize the covert directional spoofing on UAV equipped with tightly-coupled GNSS and inertial measurement unit (IMU), a GNSS covert spoofing algorithm is proposed. The algorithm includes a fixed-point four-step spoofing strategy and pays attention to avoiding the least-squares residual receiver autonomous integrity monitoring (LSR-RAIM) of UAV, so as to ensure the concealment of the deception process. Through the construction of experimental platform and experimental analysis, the effectiveness and concealment of the algorithm are verified. The experiment shows that the spoofing yaw angle error of GNSS covert directional spoofing is −0.03°. In addition, the spoofer has effectively avoided the LSR-RAIM of UAV system in the whole process, and the evasion rate has reached 100% to achieve covert spoofing. For the actual spoofing scenario application, the spoofing yaw angle error can meet the actual needs. Future work will focus on the research and design of more complex covert spoofing algorithms to meet the spoofing needs of UAV equipped with more complex navigation systems and antispoofing techniques.

Preliminary Analysis of BDS-3 Performance for ARAIM

<h3>Abstract</h3> To support the operation of advanced receiver autonomous integrity monitoring (ARAIM), an integrity support message indicating a minimum performance level of satellite constellation is required for aircraft navigation. With BDS-3 providing worldwide service since July 2020, it is desirable to undertake a detailed study on its signal-in-space range error characteristics and prior fault probabilities for ARAIM. The latest accuracy criteria released by the China Satellite Navigation Office in May 2021 is validated by the 27 MEO and IGSO satellites in orbit from July 2020 to June 2021, in which 10 single satellite faults were identified and analyzed in detail with no constellation fault found. Based on this one-year data, the probability of single satellite faults and constellation faults can be initially set as 8 × 10⁻⁵ and 1 × 10⁻³, respectively, for BDS-3 in ARAIM.

Evaluation of the weighted least square based Receiver Autonomous Integrity Monitoring (RAIM) for single frequency GNSS receivers

Single frequency receivers for Global Navigation Satellite System (GNSS) are low-cost and easily accessible, deeming them cost-effective for commercial applications requiring localization and tracking. However, the positioning solution these receivers provide must conform to desired reliability standards. A reliability check is essential in the signal-degraded environment, where a position fix might be unacceptably inaccurate. Under such conditions, faulty measurements need to be identified and excluded to ensure the system's integrity and get a reliable solution. In this paper, a Receiver Autonomous Integrity Monitoring (RAIM) scheme is devised that employs different tests. The first test performs a global sweep on all epochs and raises the alarm after detecting the fault. Next, a group of tests are performed to identify the satellite responsible for producing the faulty measurements. Once identified, it is then excluded from the measurements to maintain the reliability of the positioning provided by the receiver. The results indicate that a polling scheme based on multiple tests identifies the faulty satellite correctly and minimizes the false alarm rate if either of these tests is performed individually.

RAIM Fault Detection and Exclusion with Spatial Correlation for Integrity Monitoring

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

ARAIM Stochastic Model Refinements for GNSS Positioning Applications in Support of Critical Vehicle Applications.

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.

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.

Detection and Mitigation of GNSS Spoofing Attacks in Maritime Environments Using a Genetic Algorithm

Due to the high reliance of daily activities on the Global Navigation Satellite System (GNSS), its security is one of the major concerns for research and industry. Most navigation and mobile-driven location-based services use GNSS to render services. Due to the low power and easy access of GNSS signals, these signals are vulnerable to spoofing and other types of attacks. Recently many GNSS spoofing attacks have been identified in road- and maritime-based environments. This study provides a technique to detect and counter the GNSS spoofing attack in the maritime environment. This technique uses the Receiver Autonomous Integrity Monitoring (RAIM) model with Least Square Estimation (LSE) and Proportional Integral Derivative (PID) Control to detect the spoofing attack. The proposed technique is based on the concept of a genetic algorithm and navigation devices, such as inertial sensors and pilot options for the ship. A case study using the AIS dataset and simulation using MATLAB and NS3 is provided to validate the performance of the proposed approach. Nine different voyages from the AIS dataset were considered to check the accuracy and performance of the proposed algorithm. The accuracy of the proposed technique was analyzed using the correctly identified attack. The result shows that the proposed technique identifies spoofing attacks with an average value of 90 percent. For result analysis the considered nine routes were traversed multiple times. Root mean square error is used to calculate the positional mismatch (error rate). Based on the combined results analysis, the average value of RMSE is 0.28. In a best-case scenario, the proposed approach provides an RMSE value of 0.009.

Bayesian fault-tolerant protection level for multi-constellation navigation from integrity perspective

The multi-constellation is anticipated to enhance navigation performance by providing abundant satellites in view. However, with this comes the necessity of considering simultaneous fault events in integrity risk assessment when applying the receiver autonomous integrity monitoring. Meanwhile, the integrity monitoring efficacy remains vulnerable to the uncertainty of prior fault probability in multi-constellation navigation. In this regard, this paper proposes a Bayesian approach for the multi-constellation navigation at the user segment to strengthen the evaluation robustness of integrity risk. We bound the Bayesian posterior probability of fault hypotheses that comprise multiple concurrent faults (satellite or constellation faults) without assuming an optimistic prior fault distribution. This posterior probability bound leads to an associated Bayesian integrity risk. Following that, a fault-tolerant estimate accounting for multiple faults and nominal biases is derived to minimize the Bayesian integrity risk. Finally, we establish a Bayesian fault-tolerant protection level to bound the position estimate error and assess the integrity availability, resisting the uncertainty in the prior fault probability. Results validate the robustness improvement the proposed Bayesian approach fulfills compared with the advanced receiver autonomous integrity monitoring in four cases. In addition, the Bayesian approach can lower the protection level at the cost of an offset from the least-square estimate while maintaining the same position accuracy.

A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory

The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state information is known, and they need to establish state iterators, which leads to low computational efficiency. Meanwhile, in multi-sensor fusion, if the sampling rates are different, the change in the dimension of the observation matrix must be considered, and the calculation is complex. Based on state-domain consistency theory, this paper proposes the State-domain Robust Autonomous Integrity Monitoring by Extrapolation (SRAIME) method for identifying slow-growing gross errors for GNSS/accelerometer integrated deformation monitoring. Compared with the traditional gross error detection method, the proposed method constructs state test statistics based on the state estimated value and the state predicted value, and it directly performs gross error identification in the state domain. This paper deduces the feasibility of the proposed method theoretically and verifies that the proposed method does not need to consider the dimension change of the observation matrix in gross error detection. Meanwhile, in the excitation deformation experiments of the Suntuan River Bridge in Anhui and the Wilford Bridge in the United Kingdom, the slow gradient of the slope was added to the measurement domain, and the traditional AIME method and the method proposed in this paper were adopted for the gross error identification of the GNSS/accelerometer fusion process. The results demonstrate that both methods can effectively detect gross errors, but the proposed method does not need to consider the dimensional change in the observation matrix during the fusion process, which has better applicability to GNSS/accelerometer integrated deformation monitoring.