Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.