Search

The Hilbert space embedding provides a powerful and flexible tool for dealing with the nonlinearity and high-order statistics of random variables in a dynamical system. The kernel Kalman filtering based on the conditional embedding operator (KKF-CEO) shows significant performance improvements over the traditional Kalman filters in the noisy nonlinear time-series prediction. However, KKF-CEO based on the minimum mean-square-error (MMSE) criterion is sensitive to the outliers or heavy-tailed noises. In contrast to the MMSE criterion, the maximum correntropy criterion (MCC) can achieve more robust performance in the presence of outliers. In this paper, we develop a novel kernel Kalman-type filter based on MCC, referred to kernel Kalman filtering with conditional embedding operator and maximum correntropy criterion (KKF-CEO-MCC). The proposed KKF-CEO-MCC can capture higher order statistics of errors and is robust to outliers. In addition, two simplified versions of KKF-CEO-MCC are developed, namely, KKF-CEO-MCC-O and KKF-CEO-MCC-NA. The former is an online approach and the latter is based on Nyström approximation. Simulations on noisy nonlinear time-series prediction confirm the desirable accuracy and robustness of the new filters. [ABSTRACT FROM AUTHOR]

Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional random Fourier feature space (RFFS) such that the network structure of filters is fixed while achieving similar performance with kernel adaptive filters. The commonly used error criterion in RFAFs is the well-known minimum mean-square error (MMSE) criterion, which is optimal only under the Gaussian noise assumption. However, the MMSE criterion suffers from instability and performance deterioration in the presence of non-Gaussian noises. To improve the robustness of RFAFs against large outliers, the maximum correntropy criterion (MCC) is applied to RFFS, generating a novel robust random Fourier filter under maximum correntropy (RFFMC). To further improve the filtering accuracy, a random-batch RFFMC (RB-RFFMC) is also presented. In addition, a theoretical analysis on the convergence characteristics and steady-state excess mean-square error of RFFMC and RB-RFFMC is provided to validate their superior performance. Simulation results illustrate that RFFMC and its extension provide desirable filtering performance from the aspects of filtering accuracy and robustness, especially in the presence of impulsive noises. [ABSTRACT FROM AUTHOR]