Non-uniform l(21)-norm constraint based underwater acoustic channel adaptive estimation

It is known that the underwater acoustic channels (UAC) exhibit non-uniform cluster-sparse characteristics, meaning that the channel impulse response (CIR) typically consists of a large number of nearzero taps and with only a few none-zero ones assemble in clusters in a structured manner, moreover, the cluster structure is non-uniformly distributed on the time domain. In this paper, an improved proportionate affine projection algorithm (IPAPA) with the non-uniform l(21)-norm constraint is proposed for non-uniform cluster-sparse UAC estimation. Firstly, the auxiliary channel information is obtained via the correlation of training sequence, whereby the priori cluster-sparse structure positioning of UAC is realized. Then the non-uniform l(21)-norm is added on the IPAPA for the final accurate channel estimation: it encourages correlation among coefficients inside each group via the l(2) norm and facilitates sparsity across all groups using the l(1) norm. The results of numerical simulations and sea trial show that the proposed UAC estimation algorithm can achieve a better performance in terms of mean square error (MSE) compared to existing sparse channel estimation methods. (C) 2020 Published by Elsevier Ltd.