Nonparametric and sparse signal representations in array processing via iterative adaptive approaches
Tarik Yardibi, Jian Li, Petre Stoica · 2008
This paper addresses the problem of source localization and waveform estimation in array processing applications. We present two nonparametric user parameter free algorithms, namely the iterative adaptive approach (IAA) and the maximum likelihood based IAA (IAA-ML). Both IAA and IAA-ML can work with arbitrary array geometries and uncorrelated as well as coherent sources. We extend IAA and IAA-ML to yield sparse results by using the Bayesian information criterion (BIC). Further improvements in performance can be achieved by employing the last step of the parametric RELAX algorithm initialized by the estimates of IAA and IAA-ML with BIC. Our simulations demonstrate that IAA and IAA-ML show better performance than the most competitive sparse signal representation approaches in the literature: M-FOCUSS, M-SBL and lscr1-SVD.