SRP-PHAR Combined Velocity Scanning for Locating the Shallow Underground Acoustic Source

Abstract

Shallow underground acoustic source localization is a component of near-field source localization, which is involved in numerous application fields. The positioning accuracy is mainly limited by the accuracy of time of arrival (TOA)/time difference of arrival (TDOA) extraction and velocity extraction from noisy data. The steered response power with phase transform (SRP-PHAT) is one of the most robustness and high-precision acoustic source localization approaches, which avoids extracting the TDOA in advance. But SRP-PHAT is constrained for only using under known velocity. Furthermore, it is barely possible for shallow underground sound source localization to easily obtain high-quality velocity models. This paper proposes an improved SRP-PHAT with unknown velocity (SRP-PHAT-UNVEL), which avoids extracting the TDOA and velocity in advance. SRP-PHAT-UNVEL matches the calculated time delay curve with the actual time delay curve by scanning of the candidate spatial position and the candidate velocity simultaneously, so as to maximize the output energy to fulfill the positioning. However, SRP-PHAT-UNVEL has larger computational complexity as it proceeds with the optimization of space and velocity. Since the spatial position and velocity affect the shape and the curvature of the calculated delay curve respectively, these are two relatively independent processes. Therefore, the simultaneous optimization of space and velocity can be replaced by a two-stage optimization to improve the efficiency and accuracy of SRP-PHAT-UNVEL. Spatial optimization is equivalent to the optimization of SRP-PHAT, and the spatial optimization of the bat algorithm has faster convergence rate and higher location precision than traditional methods. Velocity optimization can be achieved by the common linear search technique since the function of velocity and energy is an ideal convex function. Simulation experiment results show that the proposed method is insensitive to noise, which can achieve high accuracy of the acoustic source position and velocity simultaneously. With grouping the measured data, the proposed method can further improve the robustness and accuracy by fusing the grouping location results with principal component analysis.