A Novel Semidefinite Programming-based UAV 3D Localization Algorithm with Gray Wolf Optimization

The unmanned aerial vehicle (UAV) network has gained vigorous evolution in recent decades by virtue of its advanced nature, and UAV-based localization techniques have been extensively applied a variety fields. In most applications, the data captured UAV are only useful when associated with geographic position. Efficient low-cost positioning is great significance for development UAV-aided technology. this paper, we investigate an effective three-dimensional (3D) approach multiple UAVs propose flipping ambiguity avoidance optimization algorithm. Specifically, beacon take charge gaining global coordinates collecting distance measurements from GPS-denied UAVs. We adopt semidefinite programming (SDP)-based to estimate position target Furthermore, high noise interference causes missing pairs measurement errors, improved gray wolf (I-GWO) algorithm utilized improve accuracy. Simulation results show that proposed superior number alternative approaches.