A Track-before-Detect Strategy for Multi-sensor Passive Acoustic Localization

Abstract

Abstract This paper focuses on the multi-sensor passive acoustic localization problem. The traditional solution for the problem is to estimate the parameter of the received signal, such as the time difference of arrival (TDOA). Then the position of the target is solved by the estimated parameter. This solution is suboptimal because estimations are carried out at each sensor separately, and the fact that the received signals of different sensors originate from the same target is ignored. In this paper, we propose a track-before-detect (TBD) method. The proposed method adopts a particle filter that defines its likelihood function as the product of the output of the cross-correlation (CC) between the signal of different sensors conditioned on the particle’s state. This likelihood function is designed to ensure that the processing gain of multi-sensors can be fully obtained. Moreover, the proposed method circumvents the challenging measurement-to-track association problem faced by the method based on the estimated parameter.