Target tracking method with bearings-only measurements based on reinforcement learning

Abstract

Traditional bearings-only measurements (BOM) passive target tracking methods have an intrinsic shortage, that is, it depends on the established target models excessively. In order to solve the problem, a novel RL-based BOM tracking method is proposed. First, a reinforcement learning (RL)-based BOM target tracking framework is established, and sensor actions and rewards function are properly defined. Then, based on the typical Q-learning techniques, and Cerebellar model articulation computer (CMAC) method, a novel BOM tracking algorithm is proposed. Finally, a simulation example is provided to show effectiveness and efficiency of the proposed passive target tracking method.