CS Based Acoustic Source Localization and Sparse Reconstruction Using Greedy Algorithms

Abstract

In this paper, the problem of direction of arrival estimation of unknown sources in under water scenario, with minimum data acquisition is investigated. The scheme is based on Compressive Sampling, an emerging technique in signal processing, which asserts that data acquisition and signal reconstruction is possible with much less number of measurements. The sparse nature of the angle spectrum is effectively utilized. The target data as received by a linear array of acoustic sensors is simulated and compressed sensing is employed prior to processing. For reconstruction of compressively sampled signals, greedy algorithms like orthogonal matching pursuit, matching pursuit and weak matching pursuit are employed. The performance of these algorithms are assessed and results are compared with conventional MUSIC algorithm.