Two-Stage Solutions via Semidefinite Relaxation for Object Localization Using UAVs

In this paper, UAVs are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable, and the localization problem is highly nonlinear to the unknown parameters. To this end, the semidefinite relaxation (SDR) technique is proposed to solve the localization problem. Since the constrained relationship among the variables is difficult to be fully included in the stage-one SDR problem, we develop a novel two-stage SDR solution for this localization problem. The performance of the two-stage SDR solution is proven to be close to the Cramér-Rae Lower Bound (CRLB) accuracy at the small noise levels. The simulated results show that the two-stage SDR solution performs better than the closed-form solution, especially at high noise levels.