Cubature Kalman filter based on generalized minimum error entropy with fiducial point

Jiacheng He; Zhenyu Feng; Gang Wang; Bei Peng

doi:10.1117/12.3017921

8 December 2023 Cubature Kalman filter based on generalized minimum error entropy with fiducial point

Jiacheng He, Zhenyu Feng, Gang Wang, Bei Peng

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294314 (2023) https://doi.org/10.1117/12.3017921
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

In practical applications, factors such as pulse interference, sensor malfunctions, and other factors can lead to observation noise exhibiting a non-Gaussian distribution, which will impair the performance of the classical cubature Kalman filter (CKF) algorithm. The existing CKF algorithm exhibits some limitations in handling complex non-Gaussian noise, and its performance may be somewhat inadequate for such scenarios. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise.

Citation Download Citation

Jiacheng He, Zhenyu Feng, Gang Wang, and Bei Peng "Cubature Kalman filter based on generalized minimum error entropy with fiducial point", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294314 (8 December 2023); https://doi.org/10.1117/12.3017921

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