Adaptive Convex Combination Filter Under Minimum Error Entropy Criterion

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 steadystate 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.