A mixing regularization parameter IPNLMS for underwater acoustic MIMO channel estimation

Abstract

Less sensitivity to the sparsity of the underwater acoustic (UWA) channel is a drawback of the improved proportionate normalized least-mean-square (IPNLMS) algorithm. To address this problem, we propose an IPNLMS algorithm with a mixing regularization parameter (MRP-IPNLMS) for the multiple-input multiple-output (MIMO) UWA channel estimation. Firstly, the proposed MRP-IPNLMS exploits the sparsity of the UWA channel and integrates an approximate L0 norm into the cost function of the IPNLMS method, achieving the fast convergence in the sparse UWA channel estimation. Furthermore, we adopt a time-varying mixing regularization parameter to dynamically adjust the cost function. The proposed MRP-IPNLMS algorithm adaptively combines the fast convergence of the small regularization parameter with the low steady-state misalignment of the large regularization parameter. In addition, we evaluate the performance of the proposed algorithm in terms of its convergence, steady-state misalignment, and output signal-to-noise ratio (OSNR). Finally, simulations and real-data experiments are also conducted to demonstrate the effectiveness of the proposed algorithm.