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To improve the localization precision of unmanned aerial vehicle (UAV), anovel framework is established by jointly utilizing multiple measurements ofreceived signal strength (RSS) from multiple base stations (BSs) and multiplepoints on trajectory. First, a joint maximum likelihood (ML) of exploiting bothtrajectory information and multi-BSs is proposed. To reduce its highcomplexity, two low-complexity localization methods are designed. The firstmethod is from BS to trajectory (BST), called LCSL-BST. First, fixing the nthBS, by exploiting multiple measurements along trajectory, the position of UAVis computed by ML rule. Finally, all computed positions of UAV for differentBSs are combined to form the resulting position. The second method reverses theorder, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) ofthe joint ML method. From simulation results, we can see that the proposedjoint ML and separate LCSL-BST methods have made a significant improvement overconventional ML method without use of trajectory knowledge in terms of locationperformance. The former achieves the joint CRLB and the latter is oflow-complexity.

Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations