Abstract
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 Cramér-Rao lower bound of the estimation. In addition, it has low computational cost when compared to the other algorithms.
C. Jutten—This work has been partly funded by ERC project 2012-ERC-AdG-320684 CHESS.
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Daei, S., Babaie-Zadeh, M., Jutten, C. (2015). A MAP-Based Order Estimation Procedure for Sparse Channel Estimation. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_40
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DOI: https://doi.org/10.1007/978-3-319-22482-4_40
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