Joint Optimization of UAV Deployment and Directional Antenna Orientation for Multi-UAV Cooperative Sensing

In this paper, we consider the problem of cooperative sensing via a system of multi-unmanned aerial vehicles (UAVs), where each UAV is equipped with a directional antenna to cooperatively perform detection tasks for several targets of interest. To measure the perception ability of the system, we choose the detection probability as the metric, aiming to maximize the sum detection probability of targets by jointly optimizing UAVs' deployment and directional antenna orientations. To tackle the inherent nonconvexity of the formulated problem, we first decompose it into two sub-problems, i.e., a slave problem for optimizing the antenna orientations with a given UAVs' deployment, and a master problem for optimizing the UAVs' deployment. By virtue of the slave problem structure, an efficient block coordinate descent (BCD) algorithm is developed. Meanwhile, to deal with the lack of the closed expression of the sum detection probability with respect to the UAVs' deployment, we further develop an iterative algorithm to acquire an efficient solution with the aid of Gibbs Sampling (GS) approach. Extensive simulations demonstrate the efficacy of the proposed algorithm.

Automated summary

In this paper, the problem of cooperative sensing via a system of multi-unmanned aerial vehicles (UAVs) is considered. Each UAV is equipped with a directional antenna to perform detection tasks for multiple targets. The detection probability is chosen as the metric to measure the system's perception ability, and the goal is to maximize the sum detection probability by optimizing UAVs' deployment and directional antenna orientations. To tackle the nonconvexity of the problem, it is decomposed into two sub-problems and efficient algorithms are developed using block coordinate descent and Gibbs Sampling approaches.