Residual‐based multi‐filter methodology for all‐source fault detection, exclusion, and performance monitoring

Jurado, Juan; Raquet, John; Kabban, Christine M. Schubert; Gipson, Jonathon S.

doi:10.1002/navi.384

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…Jurado et al [15] were interested in presenting a Solution Separation-based FD method without the assumptions of simultaneously redundant, synchronous sensors with valid measurement models and without constraining the statistical distribution of the faults. In the fault detection procedure, they refrained to rely on competing distributions (e.g.…”

Section: On Fault Detection and Threshold Optimizationmentioning

confidence: 99%

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Bosdelekidis

Bryne

Sokolova

et al. 2022

J Intell Robot Syst

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Integrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety.

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Section: On Fault Detection and Threshold Optimizationmentioning

confidence: 99%

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Bosdelekidis

Bryne

Sokolova

et al. 2022

J Intell Robot Syst

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“…A ubiquitous example is Honeywell's Inertial/GPS Hybrid (HIGH) concept which was commercially successful (Call et al, 2006). The uncorrupted subfilter guarantee provided by the ARMAS framework for a single simultaneous fault enables SAARM to extend a guarantee for all-source position integrity (Jurado et al, 2020b). The previously stated fault exclusion axiom is, thus, extended:…”

Section: Observability Layer 2 Sub-subfilter Bankmentioning

confidence: 99%

“…Assuming at least one of the subfilters is informed entirely by properly modeled, uncorrupted sensors, then at least one subfilter contains consistent state estimation error statistics (Jurado et al, 2020b). If the states of interest in each Layer 2 subfilter are observable and stabilizable, then each Layer 1 subfilter inherits these properties.…”

Section: Observability Layer 2 Sub-subfilter Bankmentioning

confidence: 99%

“…This framework is resilient to corruption from mismodeled, uncalibrated, and faulty sensors, accomplished by combining sensor validation, fault detection and exclusion (FDE), recalibration, and remodeling modes into a single architecture. Sensor-agnostic all-source residual monitoring (SAARM) was designed to provide all-source FDE and navigation integrity functions within the ARMAS framework (Jurado et al, 2020b). In this context, all-source navigation resiliency is the ability to maintain consistent all-source FDE operations with the ability to recover failed sensors.…”

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confidence: 99%

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Resilience Monitoring for Multi-Filter All-Source Navigation Framework With Assurance

Gipson

Leishman

2022

navi

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The Autonomous and Resilient Management of All-source Sensors (ARMAS) framework monitors residual-space test statistics across unique sensor-exclusion banks of filters (known as subfilters) to provide a resilient, fault-resistant all-source navigation architecture with assurance. A critical assumption of this architecture, demonstrated in this paper, is fully overlapping state observability across all subfilters. All-source sensors, particularly those that only provide partial state information (altimeters, TDoA, AOB, etc.), do not intrinsically meet this requirement. This paper presents a novel method to monitor real-time overlapping position state observability and introduces an observability bank within the ARMAS framework, known as stable observability monitoring (SOM). SOM uses a monitoring-epoch stability analysis to provide an intrinsic awareness to ARMAS of the capabilities of the fault detection and exclusion (FDE) functionality. We define the ability to maintain consistent all-source FDE to recover failed sensors as navigation resilience. A resilient FDE capability is one that is aware of when it requires more sensor information to protect the consistency of the FDE and integrity functions from corruption. SOM is the first demonstration of such a system for all-source sensors that the authors are aware of. A multi-agent 3D environment simulating both GNSS and position and velocity alternative navigation sensors was created and individual GNSS pseudorange sensor anomalies are utilized to demonstrate the capabilities of the novel algorithm. This paper demonstrates that SOM seamlessly integrates within the ARMAS framework, provides timely prompts to augment new sensor information from other agents, and indicates when framework stability and preservation of all-source navigation integrity are achieved.

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“…Furthermore, these methods have been further developed. In [15], a multi-sensors RB method is presented to obtain the FDE requirements, and a fault-tolerant method is obtained for the federal KF [16]. In addition, regarding MHSS, research achievements have also been made.…”

Section: Introductionmentioning

confidence: 99%

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

Yang,

Zhang,

et al. 2024

Meas. Sci. Technol.

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In the civil aviation system, the Inertial Measurement Unit (IMU)/ Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, a redundant IMUs/GNSS integrated navigation system is susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU fault. Additionally, dealing with correlated measurements between redundant construction presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation system. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace of covariance of primary filter. An optimal allocation scheme of continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

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Residual‐based multi‐filter methodology for all‐source fault detection, exclusion, and performance monitoring

Cited by 16 publications

References 23 publications

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Resilience Monitoring for Multi-Filter All-Source Navigation Framework With Assurance

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

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