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Adaptive sparse system identification (ASIDE) techniques have been successfully applied in many applications, such as sparse channel estimation and radar target detection. Normalized least mean fourth (NLMF)-type algorithms are considered as one of the stable ASIDE techniques even at low signal-to-noise ratio (SNR). However, the convergence capability of sparse NLMF algorithms is severely decreased...
Adaptive sparse system identification (ASIDE) techniques have been successfully applied in many applications, such as sparse channel estimation and radar target detection. By using ℓ1-norm sparse constraint, two sparse normalized least mean fourth (NLMF)-type algorithms, i.e., zero-attracting NLMF (ZA-NLMF) and reweighted ZA-NLMF (RzA-NLMF) have been developed. The two algorithms can achieve fast...
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter,...
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