Journal
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 9, Issue 4, Pages 709-718Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004389
Keywords
Fusion filter; influence function; Kalman filter (KF); robustness; unbiased finite impulse response (FIR)
Categories
Funding
- National Natural Science Foundation of China [61973136, 61991402, 61833007]
- Natural Science Foundation of Jiangsu Province [BK20211528]
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In this paper, the Kalman filter and unbiased finite impulse response filter are fused to improve robustness against uncertainties. The proposed influence finite impulse response filter does not require noise statistics and shows adaptive performance in switching between estimates based on operating conditions. It provides state estimates of best accuracy among all the compared methods.
In this paper, the Kalman filter (KF) and the unbiased finite impulse response (UFIR) filter are fused in the discrete-time state-space to improve robustness against uncertainties. To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics, we attempt to find a way to fuse without using noise statistics. The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response (IFIR) filter. The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process, showing that a critical feature of the UFIR filter is inherited. One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method. It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions. Moreover, the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.
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