Localization of near-field sources in sonar data using the sparse representation framework

This paper uses the sparse representation framework to investigate localization of near-field sources (e.g., underwater bottom or buried targets) from the data captured using two uniform linear sensor (hydrophone) subarrays. The connection between the two array steering transformation matrices of the near-field sources corresponding to the two subarrays are first analyzed using the second Taylor expansion. This connection allows the construction of a new equivalent far-field steering matrix for each near-field source, hence converting the near-field source localization problem to a more convenient far-field one. Next, the relationship between the signals observed by the two subarrays and by the new constructed far-field directional matrix is examined, leading to the conclusion that the angle of arrival (AoA) estimation can be cast into finding a solution to a sparse representation problem, where the actual AoA is considered as an entry in a complete dictionary formed by assuming a source is present in every angle. Simulation results are presented to demonstrate the effects of different noise levels and number of sensors on the accuracy of the AoA estimation. Finally, the effectiveness of the developed method is demonstrated on a sonar data set collected in a controlled environment to detect and localize several minelike objects placed proud on the pond floor.