稀疏最小均方四阶算法实现自适应稀疏感知的新方法
Novel Realization of Adaptive Sparse Sensing with Sparse Least Mean Fourth Algorithm
摘要：
非线性稀疏感知（NSS）技术已被用于实现压缩感知（CS）在许多应用中，如雷达成像和稀疏信道估计。与NSS不同的是，本文提出了一种自适应稀疏感知（ASS）方法，该方法使用重新加权的零吸引归一化最小均值四分之一（RZA-NLMF）算法，该算法取决于几个给定的参数，即，重权因子、正则化参数和初始步长。首先，基于独立的假设，Cramer-Rao下界（CRLB）推导的性能比较。此外，重新加权因子的选择方法，提出了实现鲁棒估计性能。最后，为了验证该算法，给出了基于Monte Carlo的计算机模拟，表明ASS实现了更好的均方误差（MSE）性能比NSS。
英文摘要：
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.
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