An Outlier Robust Finite Impulse Response Filter with Maximum Correntropy

Yanda Guo, Xuyou Li, Qingwen Meng
2021 IEEE Access  
Non-Gaussian noise is common in industrial applications, and it is a severe challenge to existing state estimators. In this paper, a novel robust maximum correntropy finite impulse response (MCFIR) filter is proposed to deal with the state estimation problem in the linear state-space system corrupted by outliers. The filter operates as a finite memory form, and thus it obtains superior immunity to noise statistics and process uncertainties than existing Kalman-like robust filters. Gaussian
more » ... ntropy is adopted to generate a new cost function, which improves the filter robustness to outlier interference. We derive an unbiased MCFIR filter that ignores noise statistics and propose an improvement bias-constrained MCFIR filter to achieve better estimate accuracy. To improve the filtering performance degradation caused by improper kernel size, an adaptive kernel size algorithm is further proposed, which adjusts the bandwidth within a specific range adaptively and achieves significant improvement in the MCFIR filter. An illustrative example based on moving target tracking is presented to evaluate the performance of the proposed filter, and simulation results confirmed that the MCFIR filter obtained superior immunity to outliers than the existing robust filters. INDEX TERMS Kalman filter, finite impulse response, maximum correntropy criterion, state estimation. in 2016, where he is currently pursuing the Ph.D. degree. His current research interests include signal processing, process control, and their applications in navigation technology, such as strapdown inertial navigation systems and integrated navigation. XUYOU LI received the B.E. and M.E. degrees in communication engineering and the Ph.D. degree in navigation, guidance, and control from the . Her current research interests include signal processing, information fusion, and their applications in navigation technology, such as inertial navigation and integrated navigation.
doi:10.1109/access.2021.3053212 fatcat:s46ttxqd4jhsxkf4aewl5agl74