Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning

Abstract

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results.

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

“…Currently, these efforts towards direction finding can be roughly grouped into two categories, i.e., subspace technologies [5][6][7][8] and sparse signal recovery (SSR) attempts [9][10][11][12][13]. The former are represented by the multiple signal classification (MUSIC) algorithm [5] and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [6], officially opening the era of super-resolution direction finding.…”

“…Motivated by the potential spatial sparsity of the targets, the SSR perspective based on the principle of Compressed Sensing (CS) [14,15] came into being to solve the above problem. Subsequently, a set of sparsity-aware estimators are structured, including convex optimization attempts [9][10][11] and sparse Bayesian learning (SBL) efforts [12,13]. Simultaneously, plenty of research results have shown that this not only improves the estimation accuracy under the conditions of undesirable SNR and snapshots, but also enhances the robustness to coherent sources [16].…”