Mixed Sources Localization Based on Sparse Signal Reconstruction

Abstract

In this letter, a novel mixed sources localization method based on sparse signal reconstruction is presented, which can efficiently estimate direction-of-arrival (DOA) and range parameters of near-field and far-field sources. By constructing the cumulant domain data of array which is only related to DOA parameters of mixed sources, we obtain DOA estimation of all sources using the weighted <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization. And then, a mixed overcomplete matrix on the basis of DOA estimation is introduced in the sparse signal representation framework to estimate range parameters and distinguish far-field sources from mixed sources. Compared with the two-stage MUSIC algorithm, the proposed method can provide improved accuracy and resolve closely spaced sources. The simulation results show the effectiveness of our method.