Receding horizon controller using particle swarm optimization for closed-loop ground target surveillance and tracking

This paper investigates the problem of non-myopic multiple platform trajectory control in a multiple target search and track setting. It presents a centralized receding discrete time horizon controller (RHC) with variable-step look-ahead for motion planning of a heterogeneous ensemble of airborne sensor platforms. The controller operates in a closed feedback loop with a Multiple Hypothesis Tracker (MHT) that fuses the disparate sensor data to produce target declarations and state estimates. The RHC action space for each air vehicle is represented via maneuver automaton with simple motion primitives. The reward function is based on expected Fisher information gain and priority scaling of target tracks and ground regions. A customized Particle Swarm Optimizer (PSO) is developed to handle the resulting non-Markovian, time-varying, multi-modal, and discontinuous reward function. The algorithms were evaluated by simulating ground surveillance scenarios using representative sensors with varying fields of view and typical target densities and motion profiles. Simulation results show improved aggregate target detection, track accuracy, and track maintenance for closed-loop operation as compared with typical open-loop surveillance plans.