Efficient Relative Fingerprinting Based UAV Localization via Tensor Completion

Recently, unmanned Aerial Vehicles (UAVs) localization is becoming a major research focus. In this paper, we propose a novel efficient relative fingerprinting-based passive UAV localization algorithm via a tensor completion approach. We first introduce a new relative fingerprint framework by exploring the correlations between the UAV fingerprint and the fingerprint database, the correction factors can be achieved to apply the fingerprint idea into the passive localization case. Then, we exploit the spatial correlation of the RSS data and propose a new training scheme which utilizes tensor completion. Simulation results highlight the superior performance of the proposed algorithm in terms of reconstruction error and localization accuracy.