Novel Realization of Adaptive Sparse Sensing with Sparse Least Mean Fourth Algorithm

TL;DR

This paper introduces an adaptive sparse sensing method using a reweighted zero-attracting normalized least mean fourth algorithm, demonstrating improved MSE performance over traditional nonlinear sparse sensing techniques.

Contribution

It proposes a novel adaptive sparse sensing algorithm with a reweighted zero-attracting least mean fourth approach and a new reweighted factor selection method for enhanced robustness.

Findings

ASS achieves significantly better MSE than NSS.

CRLB derived for performance benchmarking.

Simulation results confirm robustness and improved accuracy.

Abstract

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. 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 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.