Adaptive convex combination filter under minimum error entropy criterion

Siyuan Peng, Zongze Wu, Yajing Zhou, Badong Chen

Abstract: Minimum error entropy (MEE) is a robust adaption criterion and has been successfully applied to adaptive filtering, which can outperform the well-known minimum mean square error (MSE) criterion especially in the present of non-Gaussian noise. However, the adaptive algorithms under MEE are still subject to a compromise between convergence speed and steady-state mean square deviation (MSD). To address this issue, we propose in this paper an adaptive convex combination filter under MEE (CMEE), which is derived by using a convex combination of two MEE-based adaptive algorithms of different step-sizes. Monte Carlo simulation results confirm that the new algorithm can achieve fast convergence speed while keeping a desirable performance.

External IDs:dblp:conf/icdsp/PengWZC16