Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering

As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.

Automated summary

This article introduces a second-order Volterra (SOV) filter based on a recursive maximum correntropy (RMC) algorithm, and proposes two algorithms with variable forgetting factors (VFF) to enhance tracking capability. Simulation results demonstrate that the proposed algorithms outperform existing ones in impulsive noise environments and/or time-varying systems.