Reinforcement Learning Based UAV Swarm Enabled 3-D Multimodal Jamming Detection

Reinforcement learning based jamming detection that chooses the test threshold to evaluate the received signal strength indicator (RSSI) and the packet loss rate is inaccurate for unmanned aerial vehicles (UAVs) lacking target indication against smart jammers. In this paper, we propose a UAV swarm-enabled multimodal jamming detection scheme to optimize the test thresholds based on vision information such as object classes and relative distances, along with the RSSI, the channel gain and the communication performance, including packet delivery ratio, bit error rate and packet delivery delay. A machine learning classifier is used to assess the joint variation among RSSI, channel gain and communication performance, with the output outlier score compared with the test threshold to detect the jammer. The detection results shared from neighboring UAVs are exploited in the update of policy distribution to refine jamming signal resolution and thus reduce the miss detection rate. The utility bound is derived based on the Nash equilibrium of the jamming detection game between the UAV swarm and the jammer. Experimental results based on 5 UAVs to detect a smart jammer show that our proposed scheme enhances the detection accuracy compared with the benchmarks.