UAV Deployment Strategy for Range-Based Space-Air Integrated Localization Network

Unmanned aerial vehicles (UAV) deployment is of pivotal importance in the promising space-air integrated localization network (SAILN), which is a typical partially controllable network and supports 3- dimensional (3D) localization. To improve the localization accuracy for specific area or user, several UAVs need to be deployed. This paper proposes an iterative UAV deployment strategy for SAILN, which can minimize the localization error by determining accurate 3D coordinate information (elevation and azimuth angles, distance) for all the supplementary UAVs. Specifically, based on the analysis of accuracy increment when a new UAV is added into SAILN, the genetic algorithm (GA) is leveraged to find its optimal geometric position in a constrained area that can maximize the accuracy increment. Then, UAVs are iteratively added until the desired number, i.e., the quantity budget of deployed UAVs, is achieved. Simulation results demonstrate that the proposed UAV deployment strategy provides considerably better localization accuracy compared with uniform angular arrays (UAA) and random deployment (RD).