Soft Actor-Critic-Based Continuous Control Optimization for Moving Target Tracking

Abstract

In the field of cognitive electronic warfare (CEW), unmanned combat aerial vehicle (UCAV) realize moving targets tracking is a prerequisite for effective attack on the enemy. However, most of the traditional target tracking use intelligent algorithms combined with filtering algorithms leads to the UCAV flight motion discontinuous and have limited application in the field of CEW. This paper proposes a continuous control optimization for moving target tracking based on soft actor-critic (SAC) algorithm. Adopting the SAC algorithm, the deep reinforcement learning technology is introduced into moving target tracking train. The simulation analysis is carried out in our environment named Explorer, when the UCAV operation cycle of is 0.4 s, after about 2000 steps of iteration, the success rate of UCAV target tracking is above 92.92%, and the tracking effect is improved compared with the benchmark.