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Sensor Management Based on Convex Optimization via PCRLB and Joint Interception Probability

When a multi-sensor cooperative tracking system is used in situation assessment, in order to effectively avoid active sensors being intercepted and attacked by enemy, a multi-sensor management method with multiple constraints is proposed. Firstly, the posterior Cramer Rao lower bound (PCRLB) of state estimation is introduced to establish the problem of minimizing target tracking error. Secondly, the joint interception probability is introduced to form the constraint, and then the multi-sensor and multi-target allocation optimization problem is constructed. Finally, the original nonconvex problem is relaxed into the convex problem, and an allocation scheme is obtained by convex optimization techniques. Results show that the method combining interception probability and convex optimization can ensure multi-sensor scheduling safely and effectively.