Weighted Fusion Robust Incremental Kalman Filter

Xiaojun SUN; Han ZHOU; Haibin SHEN; Guangming YAN

doi:10.11999/JEIT200122

Volume 43 Issue 12

Dec. 2021

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Xiaojun SUN, Han ZHOU, Haibin SHEN, Guangming YAN. Weighted Fusion Robust Incremental Kalman Filter[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3680-3686. doi: 10.11999/JEIT200122

Citation:

Xiaojun SUN, Han ZHOU, Haibin SHEN, Guangming YAN. Weighted Fusion Robust Incremental Kalman Filter[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3680-3686. doi: 10.11999/JEIT200122

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Weighted Fusion Robust Incremental Kalman Filter

doi: 10.11999/JEIT200122

Xiaojun SUN^{1, 2},
Han ZHOU^{1
,
,},
Haibin SHEN¹,
Guangming YAN^{1, 2}

1.
Electrical Engineering Institute, Heilongjiang University, Harbin 150080, China
2.
Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61104209), The Special Funds of Heilongjiang University of Basic Scientific Research Expenses for Colleges and Universities in Heilongjiang Province (2020-KYYWF-0998)

Received Date: 2020-02-21
Rev Recd Date: 2021-03-07

Available Online: 2021-03-29

Publish Date: 2021-12-21

Abstract

Abstract

Under certain environmental conditions, when the measurement equation of the system is not verified or calibrated, the use of the measurement equation will often produce unknown system errors, resulting in large filtering errors. Similarly, when the noise variance of the system is uncertain, the performance of the filter will deteriorate, and even cause the filter divergence. The introduction of incremental equation can effectively eliminate the unknown measurement error of the system, so that the state estimation of system under poor observation condition with unknown measurement error can be transformed into the state estimation of incremental system. In this paper, a robust incremental Kalman filter based on incremental equation is proposed for linear discrete systems with unknown measurement error and unknown noise variance. Then, based on the linear minimum variance optimal fusion criterion, a weighted fusion robust incremental Kalman filtering algorithm is proposed. Simulation results show the effectiveness and feasibility of the proposed algorithm.
- Information fusion,
- Weighted fusion,
- Systems under poor observation condition,
- Incremental filtering,
- Robustness

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References(24)

References

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