Optimizing UAV Deployment for Enhanced Detection Performance in Multi-UAV Cooperative Sensing

In this work, we enhance cooperative sensing capabilities in multi Unmanned Aerial Vehicles (UAVs) systems, with each UAV mounted with a directional antenna to detect multiple targets cooperatively. To improve detection accuracy in dynamic environments with noise fluctuations, we adopt an eigenvalue-based detection technique. We maximize total detection probability by simultaneously optimizing the spatial deployment of UAVs and their antenna orientations. To address the inherent non-convexity of this problem, we propose an iterative Particle Swarm Optimization (PSO)-based approach with a penalty method for its fitness function. The proposed approach effectively navigates complex search spaces, managing spatial and antenna constraints to guide solutions toward global optima. Monte Carlo simulations demonstrate that our PSO-based algorithm outperforms current techniques, achieving superior detection performance and robust sensing capabilities. Notably, the proposed scheme achieves a 7.94% improvement with 2 UAVs and a 7.21 % improvement with 3 UAVs over the alternating direction penalty method (ADPM)-based scheme, highlighting its effectiveness even with fewer UAVs deployed for cooperative sensing.