LAUSR

This work considers the long baseline (LBL) acoustic localization problem. In the classical LBL localization approach, the time of arrival (TOA) of direct sound from each buoy is estimated independently, and the estimated TOAs are then utilized to solve for the target’s position. This approach ignores the fact that all of the estimated TOAs from different buoys correspond to the same target and thus are intrinsically correlated. To address this shortcoming, a novel localization framework based on track-before-detect (TBD) theory is established in this work, which allows the target location to be directly determined. Since it is completely freed from the traditional localization paradigm, some of the persistent and challenging problems faced by classical localization methods, such as direct sound selection and unacceptable false alarm/detection tradeoffs caused by threshold setting, can be circumvented. The effectiveness of the TBD approach is validated on both simulated and real datasets. The experimental results indicate that the proposed approach can track targets continuously and accurately and can be effectively implemented even under challenging conditions.