Mission planning for cued search of cooperative anti-stealth detection based on PPSO-MPC

To solve the cued search problem when ESMs and radars cooperate with each other in anti-stealth detection, a MPC-based (Model Predictive Control) mission planning frame for cued search is proposed, and the targets' states predictive model and on-line receding optimization model are established based on the MPC theory. Then, this paper puts forward an improved parallel PSO (Particle Swarm Optimization) algorithm to solve the problem. Concretely, a high-dimensional matrix mode is designed for particle coding, a scale-factor is imported for boundary restriction, a probabilistic model is proposed for processing discrete variable, and a new multi-swarm parallel strategy called MM-SS (Multi-Master-Single-Slave) is presented for promoting optimization efficiency. Experiments show that the established model realizes an efficient control of multi-radars in condition of uncertainty and multiple targets, and that the proposed algorithm can solve the receding optimization problem efficiently. That is, the validity of the model and algorithm is demonstrated.