An Outlier Robust Finite Impulse Response Filter With Maximum Correntropy

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 correntropy 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.