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

Guan Gui, Li Xu, Fumiyuki Adachi

研究成果: Article査読

14 被引用数 (Scopus)

抄録

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, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the 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.

本文言語English
論文番号125
ページ(範囲)1-10
ページ数10
ジャーナルEurasip Journal on Advances in Signal Processing
2014
1
DOI
出版ステータスPublished - 2014 12月 1
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • ハードウェアとアーキテクチャ
  • 電子工学および電気工学

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