Abstract
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 language | English |
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Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Eurasip Journal on Advances in Signal Processing |
Volume | 2014 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2014 Dec 1 |
Keywords
- Adaptive sparse sensing (ASS)
- Compressive sensing
- Nonlinear sparse sensing (NSS)
- Normalized least mean fourth (NLMF)
- Reweighted zero-attracting NLMF (RZA-NLMF)
- Sparse constraint
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Hardware and Architecture
- Electrical and Electronic Engineering