Robust Group Target Awareness Inference in Multi-UAV-Enabled ISCC Networks Based on Split Deep Reinforcement Learning

In an integrated sensing and communication (ISAC) system, it is often necessary to sense both the state of individual targets and the overall situation of a group target (GT) simultaneously, but the latter is more challenging due to the limited sensing capabilities and resources of a single base station. Owing to the recent rapid development of artificial intelligence (AI) and unmanned aerial vehicle (UAV) technologies, it is feasible to acquire the high-performance situation awareness of the GT by using AI to process sensing data under the cooperation of multiple UAV aerial base stations. However, due to many force majeure factors, such as power depletion, and disruptive actions, some UAVs may be disabled, which affects the situation awareness of the GT. Therefore, it is important to improve the robustness of the situation awareness. To achieve that, we consider a multi-UAV-enabled integrated sensing, communication and computation (ISCC) network, where more than one UAVs complete the group target sensing task (GTST) cooperatively while providing communication services for users. Furthermore, we deploy a pre-trained AI model to process the sensing data for improving the performance of the GTST. To enhance the robustness of the GTST, we apply split learning (SL) to divide the inference task of disabled UAVs into multiple neural network (NN) blocks that are cooperatively inferred by working UAVs. Then, we formulate a GTST completion rate maximization problem in which the trajectory, resource allocation, sensing target scheduling, beamforming, and NN layer split policy are optimized. Due to the non-convexity of the problem, we propose a collaborative multi-agent reinforcement learning scheme. The simulation results show that the proposed scheme can effectively improve the GTST performance while the minimum communication and sensing performance are guaranteed, and its performance is better than that of some benchmark schemes.