Affine projection champernowne algorithm for robust adaptive filtering

Abstract

The recently proposed affine projection Versoria (APV) algorithm has been widely used over other affine based algorithms due to its robustness against impulsive noises. However, the performance of the APV algorithm suffers from high steady state misalignment. In order to overcome this, we propose affine projection Champernowne adaptive filter (APCMAF) in which instead of taking Versoria function as a cost function we have used the probability density function of the Champernowne distribution as a cost function and data reuse technique. The proposed APCMAF algorithm provides low steady-state misalignment in impulsive noise environment. To verify the performance of the APCMAF algorithm, a set of simulation study has been done in system identification scenarios which confirms that the APCMAF provides better steady state performance with improved convergence performance over other existing algorithms in impulsive noise environments. Further, the bound of learning rate for stable convergence has been also derived and a detailed comparison of computational complexity is also presented.