Long Baseline Acoustic Localization Based on Track-Before-Detect in Complex Underwater Environments

Abstract

This paper considers the long baseline (LBL) acoustic localization problem. For this problem, traditional two-step localization methods first estimate the time of arrivals (TOAs) of the target signal and then solve the target’s position based on the estimated TOAs. Since TOA estimation is performed at each buoy independently, they are suboptimal without considering the physical basis that the TOAs estimated from different buoys all correspond to the same target position. To overcome this drawback, we propose a novel localization approach based on track-before-detect (TBD) and particle filtering (PF) theory, which directly determines the target’s position. Since this method breaks from the traditional two-step paradigm, several extra challenges arising from the two-step paradigm itself can be avoided. Specifically, we make two main contributions. First, a unique method is given for designing the likelihood function of the PF framework, which defines the likelihood function as the product of the matched filter outputs of multiple buoys conditioned on the particle states. As this design scheme is without any assumptions on the emission signal type, it can be applied to any signal type. Second, multiple models are employed to sample the particles to reduce the performance loss caused by target maneuvers. Additionally, an auxiliary variable is used to improve the sampling efficiency of the multiple models. The efficacy of the proposed method is demonstrated both in simulations and on real datasets. Experimental results show that the proposed algorithm outperforms the traditional LBL localization methods under harsh conditions.