Generalized Asymmetric Correntropy for Robust Adaptive Filtering: A Theoretical and Simulation Study

Abstract

Correntropy has been proved to be effective in eliminating the adverse effects of impulsive noises in adaptive filtering. However, correntropy is not desirable when the error between the two random variables is asymmetrically distributed around zero. To address this problem, asymmetric correntropy using an asymmetric Gaussian function as the kernel function was proposed. However, an asymmetric Gaussian function is not always the best choice and can be further expanded. In this paper, we propose a robust adaptive filtering based on a more flexible definition of asymmetric correntropy, which is called generalized asymmetric correntropy that adopts a generalized asymmetric Gaussian density (GAGD) function as the kernel. With the shape parameter properly selected, the generalized asymmetric correntropy may get better performance than the original asymmetric correntropy. The steady-state performance of the adaptive filter based on the generalized maximum asymmetric correntropy criterion (GMACC) is theoretically studied and verified by simulation experiments. The asymmetric characteristics of queue delay in satellite networks is analyzed and described, and the proposed algorithm is used to predict network delay, which is essential in space telemetry. Simulation results demonstrate the desirable performance of the new algorithm.

A Redundant Measurement-Based Maximum Correntropy Extended Kalman Filter for the Noise Covariance Estimation in INS/GNSS Integration

“…Different from the classical Gaussian kernel function, they adopted a generalized asymmetric Gaussian density function. The proposed method could achieve more accurate prediction than the classical MCC in a delay prediction simulation of a satellite network [28]. Liao et al proposed a dynamic self-tuning MCKF (DSTMCKF), which was used to improve positioning accuracy in wireless sensor networks (WSN).…”