Ranging Code Design for UAV Swarm Self-Positioning in Green Aerial IoT

Utilizing the unmanned aerial vehicle (UAV) swarm to realize location awareness of ground users (GUs) is a promising technology in green aerial Internet of Things (IoT) systems. However, a typical UAV-based positioning system is frequently applied in dense urban areas, where the traditional satellite positioning systems are severely impaired. Due to the co-frequency interference and the inevitable defect of limited resources of the positioning anchors, the UAVs positioning ability and system resource utilization suffer serious challenges. In this article, we propose a novel ranging code design for UAV swarm self-positioning, which consists of the code truncation algorithm and Greedy-based code group optimization algorithm, aiming to obtain the code group with the optimal correlation characteristics and shorter code length based on the pseudo noise (PN) code. In particular, the design can flexibly change the truncated length, so as to ensure the self-positioning accuracy with less positioning resource. To evaluate the influence of the proposed design for the self-positioning of UAVs on the aerial IoT, the root-mean-square error (RMSE) of UAVs self-positioning is provided, and based on this, the Cramer-Rao lower bound (CRLB) of GUs location estimate is derived. Numerical results demonstrate that the ranging code design is a superior way for future green aerial IoT in both positioning and resource utilization, as compared with a state-of-the-art approach.

Automated summary

In this article, a novel ranging code design for UAV swarm self-positioning is proposed, which can flexibly change the truncated length to ensure accuracy and improve resource utilization. Numerical results demonstrate that this design is a superior way for future green aerial IoT in both positioning and resource utilization, as compared with a state-of-the-art approach.