Efficient Deployment Optimization Design for Multi-UAV Cooperative Sensing System

In the face of dynamic electromagnetic environments, unmanned aerial vehicle (UAV) swarm-based sensing technologies have gained considerable attention due to their superior mobility, adaptable coverage, and reliable line-of-sight (LoS) connectivity. These advantages make UAVs well-suited for a wide range of sensing applications. However, optimizing UAV deployment to enhance sensing accuracy presents a considerable challenge for multi-UAV systems, particularly when dealing with complex target environments. This paper investigates a cooperative sensing problem within a multi-UAV framework, where multiple UAVs collaboratively perform energy detection for a set of ground targets (GTs). To evaluate the system's sensing accuracy, we use energy detection probability as the performance metric, with the objective of maximizing the network's overall detection probability through optimized UAV placement. Given the non-convex nature of the problem, we develop an efficient, low-complexity algorithm based on Gibbs Sampling (GS) to iteratively optimize UAV positions. Extensive simulation results validate the effectiveness of the proposed algorithm, demonstrating its robustness in various scenarios and providing practical insights for the design of real-world multi-UAV sensing systems.