A SINS/GNSS/VDM Integrated Navigation Fault-Tolerant Mechanism Based on Adaptive Information Sharing Factor

Lyu, Xu; Hu, Baiqing; Wang, Zheng; Gao, Duanyang; Li, Kailong; Chang, Lubin

doi:10.1109/tim.2022.3147335

Cited by 15 publications

(12 citation statements)

References 36 publications

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“…In the field of robotics, state estimation algorithms mainly include filtering-based method, such as extended Kalman filtering (EKF) [5], [13] and unscented Kalman filtering (UKF) [14], [15], and optimization-based method, such as sliding-window graph optimization [10], [16] batch graph optimization [17]. Generally, the former is suitable for applications where lower computational cost is desired, and the latter is suitable for computationally intensive tasks such as smoothing and mapping.…”

Section: A State Estimation Algorithmmentioning

confidence: 99%

Multimodal Information Fusion for High-Robustness and Low-Drift State Estimation of UGVs in Diverse Scenes

Zhong

Peng

et al. 2022

IEEE Trans. Instrum. Meas.

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Currently, the autonomous positioning of 1 unmanned ground vehicles (UGVs) still faces the problems 2 of insufficient persistence and poor reliability, especially in 3 the challenging scenarios where satellites are denied, or the 4 sensing modalities such as vision or laser are degraded. Based 5 on multimodal information fusion and failure detection (FD), 6 this article proposes a high-robustness and low-drift state 7 estimation system suitable for multiple scenes, which integrates 8 light detection and ranging (LiDAR), inertial measurement 9 units (IMUs), stereo camera, encoders, attitude and heading 10 reference system (AHRS) in a loose coupling way. Firstly, a state 11 estimator with variable fusion mode is designed based on the 12 error-state extended Kalman filtering (ES-EKF), which can 13 fuse encoder-AHRS subsystem (EAS), visual-inertial subsystem 14 (VIS), and LiDAR subsystem (LS) and change its integration 15 structure online by selecting a fusion mode. Secondly, in order 16 to improve the robustness of the whole system in challenging 17 environments, an information manager is created, which judges 18 the health status of subsystems by degeneration metrics, and 19 then online selects appropriate information sources and variables 20 to enter the estimator according to their health status. Finally, 21 the proposed system is extensively evaluated using the datasets 22 collected from six typical scenes: street, field, forest, forest-23 at-night, street-at-night and tunnel-at-night. The experimental 24 results show our framework is better or comparable accuracy 25 and robustness than existing publicly available systems. 26 Index Terms-Error-state extended Kalman filter (ES-EKF), 27 failure detection (FD) and handling, light detection and ranging 28 (LiDAR)-inertial-visual-encoder odometry, multimodal informa-29 tion fusion, state estimation. 30 I. INTRODUCTION 31 U NMANNED ground vehicles (UGVs) have been widely 32 deployed in various real world applications, such as 33

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Section: A State Estimation Algorithmmentioning

confidence: 99%

Multimodal Information Fusion for High-Robustness and Low-Drift State Estimation of UGVs in Diverse Scenes

Zhong

Peng

et al. 2022

IEEE Trans. Instrum. Meas.

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“…Experimental results show that the method obtains higher accuracy motion information compared with the traditional central difference Kalman filter (CDKF) method and covariance matching method. Lyu designed an adaptive shared-factor integrated navigation information fusion technique scheme in the literature [17], combining SINS, GNSS and vehicle dynamic model (VDM) multi-source sensors to establish an incomplete constraint-based VDM and GNSS measurement model, and the experimental results show that the algorithm has high navigation accuracy and fault tolerance. Zhang proposed an adaptive robust cubature Kalman filtering (CKF) algorithm in the literature [18] for the coarse and inaccurate noise statistical properties of the observations, and experiments show that the algorithm can enhance the stability and improve the estimation accuracy and convergence speed of the filter.…”

Section: Introductionmentioning

confidence: 99%

“…The innovation adaptive Kalman filtering algorithm based on memory attenuation (IAE-AKF) was proposed by Liu in the document [17]. This method increases the weight of the recent innovation in the whole window, and then increases the proportion of the recent innovation in the prediction of the innovation at the moment k. However, through the IAE-AKF simulation verification in section 3, it is found that the weight value accounts for more than 95% in the five moments of the approaching time, which almost ignores the predictive effect of innovation under other windows.…”

Section: Introduction To the Weight Functionmentioning

confidence: 99%

“…The memory decay-based adaptive Kalman filtering algorithm (IAE-AKF) was proposed by Liu in the literature [17], which increases the weight of the newly recent innovation in the predicted innovation at the kth moment, and thus improves the accuracy of the overall filtering algorithm. However, after the IAE-AKF simulation in section 3, it is found that the weights are overweighted to more than 95% in the five moments near the kth moment, almost ignoring the prediction of innovation under other windows, which limits the further improvement of the filtering accuracy.…”

Section: Introduction To the Weight Functionmentioning

confidence: 99%

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An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

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Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy.In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

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“…Ref. [17] applied residual chi-square detection to an improved UKF algorithm with the fading factor to improve the fault diagnosis rate of integrated navigation systems. However, the residual chi-squared test is difficult to process the slow-varying soft faults.…”

Section: Introductionmentioning

confidence: 99%

Double‐state chi‐square test based sparse grid quadrature filtering algorithm and its application in integrated navigation

Jiang

2023

IET Control Theory & Appl

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Aiming to reduce the impact that the measurement uncertainty of nonlinear system brings to filtering accuracy, a novel sparse grid quadrature filter (SGQF) algorithm based on the double‐state chi‐square test is proposed. And the strap‐down inertial navigation system (SINS)/global navigation satellite system (GNSS) integrated navigation system is used to verify the algorithm. A discrete‐time system model with uncertain measurement is constructed, then the system state through the SGQF algorithm is estimated. To determine the uncertainty of the measurement, the double‐state chi‐square test is introduced. By comparing the results of the state recursor, the chi‐square test value is obtained and it is used to update the measurement noise. Further, the system state and error covariance are re‐estimated. So far a robust sparse grid filter algorithm with statistical adjustment of the measurement noise is obtained. The proposed algorithm can overcome the influence of error statistical characteristic amplification on system filtering accuracy. Taking the SINS/GNSS integrated navigation system as the application object, off‐line simulation experiments and online flight tests verify the robustness of the proposed algorithm.

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A SINS/GNSS/VDM Integrated Navigation Fault-Tolerant Mechanism Based on Adaptive Information Sharing Factor

Cited by 15 publications

References 36 publications

Multimodal Information Fusion for High-Robustness and Low-Drift State Estimation of UGVs in Diverse Scenes

Multimodal Information Fusion for High-Robustness and Low-Drift State Estimation of UGVs in Diverse Scenes

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Double‐state chi‐square test based sparse grid quadrature filtering algorithm and its application in integrated navigation

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