A Scaled LMS Algorithm for Sparse System Identification with Impulsive Interference

new LMS algorithm is proposed to improve the accuracy of the sparse system identification with impulse interference. The algorithm adopts a scaler to filter impulse interference, the scalar is also used for identifying sparse systems. The adaptive iteration can avoid significant changes since the abrupt impulsive interference in the channel estimation. Furthermore, the proposed method searches the sparse solution by considering the system sparsity and using an approximated ℓ _0 norm constraint. Because the proposed method combines sparse constraint and impulse interference, it gradually adjusts the step size according to the gradient. The effectiveness and superiority of the proposed algorithm are confirmed for sparse system identification with impulse interference in simulations.