Robust affine projection algorithm using selectively shrunk error component

A novel robust affine projection algorithm (APA) is proposed, which selectively shrinks error components in an error vector according to their individual possibilities of being interrupted by the impulsive noise. In existing robust APAs, if there exists only one error component interrupted by the impulsive noise, all error components of an error vector are shrunk using common step sizes which are inversely proportional to the norm of the error vector. This improper scaling results in performance degradation with a high impulsive noise probability and projection order. In this paper, we derive a modified minimization criterion considering the individual possibilities of error components from a geometric interpretation. For a wide range of impulsive noise probability and a high projection order, the performance of the proposed algorithm is verified in various system identification events including an abrupt system change. The proposed algorithm showed the fastest convergence rate and the lowest steady-state mean square deviation compared to the previous robust APAs and a recent variable step-size affine projection sign algorithm.