RZA-NLMF algorithm based adaptive sparse sensing for realizing compressive sensing problems

Autor:	Gui, Guan, Xu, Li, Adachi, Fumiyuki
Rok vydání:	2014
Předmět:	Computer Science - Information Theory
Druh dokumentu:	Working Paper
DOI:	10.1186/1687-6180-2014-125
Popis:	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 reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter and initial step-size. First, based on the independent assumption, Cramer Rao lower bound (CRLB) is derived as for the trademark of performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS. Comment: 15 pages, 9 figures, submitted for journal
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1403.0190 Zobrazit plný text záznamu View this record from Arxiv