  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
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 trademark of
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.
