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

@article{Gui2014RZANLMFAA,
  title={RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing},
  author={Guan Gui and Li Xu and Fumiyuki Adachi},
  journal={EURASIP Journal on Advances in Signal Processing},
  year={2014},
  volume={2014},
  pages={1-10}
}
  • Guan Gui, Li Xu, F. Adachi
  • Published 2 March 2014
  • Computer Science, Mathematics
  • EURASIP Journal on Advances in Signal Processing
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… Expand
Novel realization of adaptive sparse sensing with sparse least mean fourth algorithm
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An adaptive sparse sensing approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., re weighted factor, regularization parameter and initial step-size is proposed for achieving robust estimation performance. Expand
Microsoft Word-Gui_WCSP2014.docx
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 sparseExpand
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