A nonlinear quaternion-based fault-tolerant SINS/GNSS integrated navigation method for autonomous UAVs

Liang, Yueqian; Jia, Yingmin

doi:10.1016/j.ast.2014.11.009

Cited by 32 publications

(17 citation statements)

References 21 publications

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“…Autonomous Integrity Monitoring by Extrapolation [10], Innovation-Based (IB) method [11], Residual-Based (RB) method [11], Solution Separation (SS) method [12,13], Quality Control (QC) [14], Generalized Likelihood Ratio (GLR) [15], Extended RAIM (ERAIM) [16], and Rate Detector (RD) method [17] are the most representative hypothesis-test FDE algorithms. Recently, some novel methods have been proposed, including Support Vector Machine (SVM) approach [18], neural network approach [19], robust estimator approaches [20][21][22] and nonlinear filter approaches [23,24]. However, these novel methods aim to improve the performance of FD in terms of time-to-detect, but make it very difficult to evaluate the integrity risk (i.e., the probability of hazardously misleading information).…”

Section: Of 21mentioning

confidence: 99%

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Wang

Zhan

Zhai

et al. 2020

Sensors

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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

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Section: Of 21mentioning

confidence: 99%

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Wang

Zhan

Zhai

et al. 2020

Sensors

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“…A magnetic compass fault detection approach for GPS/INS/Magnetic compass integrated navigation systems was proposed in [35], where the abrupt fault was supposed to be caused by the hard iron and soft iron effect. In [36], a fault tolerant Strapdown INS (SIN-S)/Global Navigation Satellite System (GNSS) approach was presented based on modified EKF and divided difference KF. However, to the best of our knowledge, the problems of fault tolerant alignment are still under research, which motivate us to study this meaningful and challenging topic.…”

Section: Introductionmentioning

confidence: 99%

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Cao

Guo

Chen

2018

Aerospace Science and Technology

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Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs subjected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability.

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“…Steps 1-6 describes the training process in detail. Then the PLSR prediction model can be obtained according to Equation (21). When the DVL fails, the PLSR predictor is working to predict the DVL measurements.…”

Section: Plsr For the Prediction Of The Dvl Measurementsmentioning

confidence: 99%

“…Finally, the regression equation of Y about X with regard to the PLSR model can be obtained according to Equation (21).…”

Section: Plsr For the Prediction Of The Dvl Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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Abstract:As a common device for underwater integrated navigation systems, Doppler velocity log (DVL) has the risk of malfunction. To improve the reliability of navigation systems, a hybrid approach is presented to forecast the measurements of the DVL while it malfunctions. The approach employs partial least squares regression (PLSR) coupled with support vector regression (SVR) to build a hybrid predictor. As the current and past calculating velocities of strapdown inertial navigation system (SINS) are taken as the predictor's inputs, PLSR is applied to cope with the situation where there exists intense relativity among independent variables. Since PLSR is a linear regression, SVR is used to predict the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the hybrid predictor is trained online as a backup, whereas during malfunctions, the predictor offers the estimation of the DVL measurements for information fusion. The performance of the proposed approach is verified with simulations based on SINS/DVL/MCP/pressure sensor (PS) integrated navigation system. The comparison results indicate that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions, thereby improving the navigation accuracy and reliability.

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A nonlinear quaternion-based fault-tolerant SINS/GNSS integrated navigation method for autonomous UAVs

Cited by 32 publications

References 21 publications

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

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