Resilient fusion navigation based on failure influence level evaluation

Wang, Rong; Xiong, Zhi; Liu, Jianye; Cao, Yuxuan

doi:10.1049/iet-rsn.2018.5161

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…The fault detection results after time alignment are shown in Figure 10 and Table 2. - 9 where M and N represent manoeuvre and non-manoeuvre, respectively; and before and after represent before and after time alignment, respectively. Figure 10 shows that after time alignment, the PFA of the fault detection model is significantly reduced.…”

Section: Time-offset Estimationmentioning

confidence: 99%

“…Because of the hostile environment and hardware damage, many types of faults of sensors will occur, including abrupt, gradual, oscillating [3,7,8], which may cause serious accidents [3]. Hence, the quick detection and isolation of these faults are highly desirable [9,10]. The Chi-square fault detection algorithm is mostly applied to integrated navigation [11].…”

Section: Introductionmentioning

confidence: 99%

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Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Chen

Wang

et al. 2021

IET Radar Sonar & Navi

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The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time-offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time-offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge-Kutta to deal with the non-linear and discretisation problem. A timeoffset estimator is designed and the observability of time-offset is analysed, showing that time-offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time-offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time-offset. Simulation results show that under multiple time-offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Time-offset Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Chen

Wang

et al. 2021

IET Radar Sonar & Navi

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“…The fault of inertial sensors may cause fatal accidents [5]. Accordingly, the fault should be detected and isolated accurately [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Ove the past few years, the research on fault detection and diagnosis of INS has attracted many attentions [6]. There have been numerous hardware redundancy-based fault detection methods for multiple homologous navigation systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

ALPF-GLRT based fault detection method for small faults applied to redundant IMUs

Li¹,

Chen²,

Wu³

et al. 2021

Meas. Sci. Technol.

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Since small faults exhibit a very close magnitude to sensor noises, the probability of missing detection (PMD) in existing methods will increase sharply in the presence of small faults. To address such a problem, this paper proposes a novel fault detection method for small faults applied to redundant IMUs. First, this method introduces an adaptive low-pass filter (ALPF) into the general likelihood ratio test (GLRT) by filtering the parity residuals of the GLRT model, thereby reducing the disturbance of sensor noises on fault detection. Subsequently, since the introduction of ALPF leads to the changes of the parity residual statistics, the covariance of parity residual should be recalculated at the respective sample instant. Lastly, to theoretically prove the superiority of the proposed method for small faults, the minimum detection bias (MDB) is derived and calculated, thereby validating that the MDB of the proposed method is lower than that of the conventional GLRT method. As indicated from the simulation results, the PMD of the proposed method decreases significantly for small faults compared with the GLRT method and the Monte Carlo PMD of the proposed method is 0.1% under the fault with the magnitude of 1 sigma, which demonstrates the effectiveness of the proposed method.

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“…In this case, the accuracy of positioning solution is approximately equal to the accuracy of the Loran‐C system that is about 100 m or more [14]. Another solution consists of data fusion algorithms such as unscented Kalman filter (UKF), unscented particle filter (UPF), particle swarm optimisation (PSO), generic algorithm (GA) and any other heuristic and non‐heuristic algorithms [15–20]. Usually, these algorithms are used in the state estimation process in the INS/GPS integrated systems.…”

Section: Introductionmentioning

confidence: 99%

Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

Majidi

Khaloozadeh

2020

IET Radar, Sonar & Navigation

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Resilient fusion navigation based on failure influence level evaluation

Cited by 6 publications

References 17 publications

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

ALPF-GLRT based fault detection method for small faults applied to redundant IMUs

Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

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