Self-Positioning for UAV Swarm via RARE Direction-of-Arrival Estimator

Abstract

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.