Optimal maneuver strategy for an autonomous underwater vehicle with bearing-only measurements

Track-before-detect (TBD) algorithms have been proven to circumvent the challenges of measurement-to-track association and have excellent robustness under harsh conditions. These benefits are aptly relevant for devel-oping unmanned passive sonar tracking systems deployed on an autonomous underwater vehicle (AUV). This work considers the optimal maneuver problem exclusively for passive sonar TBD bearing-only localization al-gorithms, which is essential for an AUV to enhance observability autonomously. To solve this problem, we derive the Fisher information matrix (FIM) of the TBD algorithms, whose determinant reflects the observability. The determinant of the FIM is utilized as a cost function to design an optimal maneuver strategy (OMS). Although the cost function is nonconvex, we demonstrate that the optimal global solution maximizing the cost function can be analytically established. In addition, the designed OMS is extended to the condition with a course constraint to consider physical limitations in practical applications. Finally, simulated and real experiments are performed to verify the effectiveness of the designed OMS.