A MAP-Based Order Estimation Procedure for Sparse Channel Estimation

Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation SCE algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori probability MAP to detect the non-zero channel tap locations while least square estimation is used to determine the values of the channel taps. Computer simulations shows that the proposed algorithm outperforms the existing algorithms in terms of normalized mean squared error NMSE and approaches Cramer-Rao lower bound of the estimation. In addition, it has low computational cost when compared to the other algorithms.

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