Adaptive Incremental Kalman Filter Based on Innovation

Xiaojun SUN; Han ZHOU; Guangming YAN

doi:10.11999/JEIT190493

Volume 42 Issue 9

Sep. 2020

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Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493

Citation:

Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493

Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493

Citation:

Xiaojun SUN, Han ZHOU, Guangming YAN. Adaptive Incremental Kalman Filter Based on Innovation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2223-2230. doi: 10.11999/JEIT190493

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Adaptive Incremental Kalman Filter Based on Innovation

doi: 10.11999/JEIT190493

Xiaojun SUN^{1, 2
,
,},
Han ZHOU¹,
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 Outstanding Youth Science Foundation of Heilongjiang University (JCL201103), The Key Laboratory of Electronics Engineering, College of Heilongjiang Province (DZZD2010-5), The Youth Science Foundation of Heilongjiang University (QL201212)

Received Date: 2019-07-02
Rev Recd Date: 2020-03-20

Available Online: 2020-08-06

Publish Date: 2020-09-27

Abstract

Abstract

Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which can effectively solve the problem of state estimation for the systems under poor observation condition. In this paper, the linear discrete incremental system with unknown noise statistics is considered. Firstly, a noise statistics estimation algorithm is proposed based on innovation. The unbiased estimation of system noise statistics can be obtained. Furthermore, a new incremental system adaptive Kalman filtering algorithm is proposed. Compared with the existing adaptive incremental filtering algorithm, the state estimation accuracy of the proposed algorithm is higher. Two simulation examples prove its effectiveness and feasibility.
- Adaptive Kalman filtering,
- Incremental filters,
- Systems under poor observation condition,
- Incremental systems,
- Filtering accuracy

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References(20)

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