Article Details

Title · Authors · Abstract · Keywords · DOI · References

Adaptive Robust Kalman Filtering Algorithm Based on Statistical Similarity

MA Langjiao, WU Junqin

(Jiangxi University of Science and Technology, Ganzhou 341000, China)

Section：Computer Technology · Vol. 2026, Issue 14 · Pages：81-85,91 · 2096-4706（2026）14-0081-06 · CLC number：TP13 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.14.016

Abstract

Noise containing outliers exhibits heavy-tailed characteristics, which leads to a decline in the performance of the optimal Kalman filter under Gaussian noise. To address the state estimation problem with unknown and time-varying noise covariance matrices, an adaptive robust Kalman filtering algorithm based on statistical similarity measurement (RSSMAKF) is proposed for non-Gaussian noise generated under the influence of outliers. The algorithm utilizes statistical similarity measurement to construct a cost function, and simultaneously estimates the posterior probability densities of the system state, process error, and observation noise. In order to adaptively suppress the influence of outliers, a τ-adaptive strategy is proposed and embedded in the estimation process of the posterior probability density function. The simulation results show that the proposed RSSMAKF algorithm performs better than other robust Kalman filtering algorithms under the mean square error criterion.

Keywords

Kalman filter; outliers; statistical similarity measurement; non-Gaussian noise; state estimation

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About the authors

Ma Langjiao (2001-), male, Han ethnicity, from Yichun, Jiangxi Province, is currently pursuing a master's degree in adaptive signal processing; Corresponding author: Wu Junqin (1966-), male, Han ethnicity, from Ganzhou, Jiangxi Province, professor, master's degree, research direction: communication and information systems, embedded systems.