Efficient multi-sensor path scheduling for cooperative target tracking

This study deals with path scheduling problem of cooperative target tracking by multiple sensors with bearings only measurements. First, the authors derive a closed-form expression of the determinant (D-optimality criterion) of the Fisher information matrix (FIM) as the cost function, which contains the knowledge of the target and the locations of sensors. Second, a penalty function is introduced to modify the cost function for threats avoidance and physics constraints are applied to limit directions of sensors. Then, an efficient strategy based on steepest descent is proposed to solve the optimisation problem. Finally, the effectiveness of the proposed algorithm is demonstrated both in localisation of a stationary target and tracking a moving target. Simulation results show the trajectories of sensors for cooperative target tracking are almost identical to a grid-based search method; however, the computational complexity is reduced by several orders of magnitude.