An optimized proportionate adaptive algorithm for sparse system identification

Proportionate-type adaptive algorithms are commonly used for the identification of sparse impulse responses, like in network and acoustic echo cancellation. In this paper, we propose an optimized proportionate LMS adaptive filter in the context of a state variable model. The algorithm follows an optimization criterion based on the minimization of the system misalignment and uses an iterative procedure for computing the proportionate factors. Consequently, it achieves a proper compromise between the performance criteria, i.e., fast convergence/tracking and low misadjustment. Simulations performed in the context of sparse system identification indicate the good behavior of the proposed algorithm.