Robust Localization and Tracking of UAVs in OTFS-based Networks

Alessandro Nordio, Carla Fabiana Chiasserini, Emanuele Viterbo
Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)
2023-11-29 00:00:00
We consider the problem of accurately localizing N unmanned aerial vehicles (UAV) in 3D space where the UAVs are part of a swarm and communicate with each other through orthogonal time-frequency space (OTFS) modulated signals. Each receiving UAV estimates the multipath wireless channel on each link formed by the line-of-sight (LoS) transmission and by the single reflections from the remaining N-2 UAVs. The estimated power delay profiles are communicated to an edge server, which is in charge of computing the exact location and speed of the UAVs. To obtain the UAVs locations and velocities, we propose an iterative algorithm, named Turbo Iterative Positioning (TIP), which, using a belief-propagation approach, effectively exploits the time difference of arrival (TDoA) measurements between the LoS and the non-LoS paths. Enabling a full cold start (no prior knowledge), our solution first maps each TDoA's profile element to a specific ID of the reflecting UAV's. The Doppler shifts measured by the OTFS receivers associated with each path are also used to estimate the UAV's velocities. The localization of the N UAVs is then derived via gradient descent optimization, with the aid of turbo-like iterations that can progressively correct some of the residual errors in the initial ID mapping operation. Our numerical results, obtained also using real-world traces, show how the multipath links are beneficial to achieving very accurate localization and speed of all UAVs, even with a limited delay-Doppler resolution. Robustness of our scheme is proven by its performance approaching the Cramer-Rao bound.
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