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

Guan Gui, Li Xu, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Article number125
Pages (from-to)1-10
Number of pages10
JournalEurasip Journal on Advances in Signal Processing
Issue number1
Publication statusPublished - 2014 Dec 1


  • Adaptive sparse sensing (ASS)
  • Compressive sensing
  • Nonlinear sparse sensing (NSS)
  • Normalized least mean fourth (NLMF)
  • Reweighted zero-attracting NLMF (RZA-NLMF)
  • Sparse constraint


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