Maximum Correntropy Unscented Filter

Xi Liu (1) , Badong Chen (1) , Bin Xu (2) , Zongze Wu (3) , Paul Honeine (4)

1 Xjtu - Xi'an Jiaotong University
2 Department of Electronic Engineering (Xiamen University)
3 SUSTech - Southern University of Science and Technology
4 DocApp - LITIS - Equipe Apprentissage

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for non-linear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearisation regression based on the maximum correntropy criterion is then used to obtain the posterior estimates of the state and covariance matrix. The satisfying performance of the new algorithm is confirmed by two illustrative examples.

Mots clés: machine learning; unscented Kalman filter (UKF); unscented transformation (UT); maximum correntropy criterion (MCC)

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]

Xi Liu, Badong Chen, Bin Xu, Zongze Wu, Paul Honeine. Maximum Correntropy Unscented Filter. International Journal of Systems Science, 2017, 48 (8), pp.1607-1615. ⟨hal-01965044⟩