Integrated Sensing and Communication for UAV Detection: A Reinforcement Learning-based Approach

The detection efficiency for unmanned aerial vehi-cles (UAV s) is lower as the targets move at low altitude and low velocity. In this paper, we propose a detection method for low-altitude small UAV s using the integrated sensing and communication (ISAC) in 5G networks. We first present a reflection wave model for UAV s in multipath environments covered by 5G networks. Then, we propose an adaptive detector based on reinforcement learning. The detector uses a reward mechanism based on the minimum signal-to-noise ratio (SNR) to construct a reinforcement learning model, and performs round-based iterative update of the parameter selection policy independent of human experience. Experiments demonstrate that the success rate of target detection of our proposed low-altitude UAV detection can achieve 91.2 %.