A novel interactive robust filter algorithm for GNSS/SINS integrated navigation

To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise bu...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering Journal of aerospace engineering, 2023-06, Vol.237 (8), p.1779-1790
Main Authors: Zhao, Bin, Zeng, Qinghua, Liu, Jianye, Gao, Chunlei, Zhao, Tianyu, Li, Rongbing
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Anomalies
Covariance matrix
Global navigation satellite system
Kalman filters
Noise measurement
Performance degradation
Performance indices
Position measurement
Robustness
Unmanned aerial vehicles
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Summary:To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise but is ineffective to abnormal measurement information. Based on the same performance index function with STKF, a measurement noise covariance matrix adaptive Kalman filter algorithm (MAKF) is presented, but it is ineffective under uncertain system noise. Furthermore, the interactive robust filter algorithm based on STKF and MAKF (IF-STKF-MAKF) is proposed, given the complementary characteristics of the above two filter algorithms. The STKF and MAKF operate in parallel based on the same system model. The filter probability of each filter is updated according to the likelihood function to perform output fusion and input interaction. The simulation and experiment results demonstrate that the IF-STKF-MAKF is effective and can achieve high estimation accuracy under both system noise anomalies and measurement information anomalies. In the vehicle experiment, the position accuracy of the proposed IF-STKF-MAKF method has been improved by more than 30% compared with KF, STKF, and MAKF. This method can also be extended to land vehicles, mobile robots, etc.
ISSN:0954-4100
2041-3025
DOI:10.1177/09544100221138133