Blockchain-Assisted Secure Distributed UAV Localization Under Byzantine and False Data Injection Attacks

Cooperative localization lets unmanned aerial vehicles (UAVs) fuse absolute and relative measurements, but the message exchange of distributed solvers is exposed to compromised UAVs. We study distributed weighted-least-squares (WLS) localization with the alternating direction method of multipliers (ADMM) under Byzantine deception, disruption, false data injection (FDI), and adaptive attacks. To mitigate these attacks, we propose a blockchainassisted ADMM in which a permissioned blockchain records one signed state per UAV and iteration, which prevents equivocation, and a relative-measurement check with a calibrated threshold screens the accepted states; a symmetric edge update makes the method independent of UAV labels. When honest neighbors accept each other, the method converges once the screening decisions stabilize and reject every compromised state, and at any fixed point its deviation from the accepted honest-subnetwork WLS solution is bounded in proportion to the screening threshold. In 1000-UAV simulations with 100–300 compromised UAVs, the proposed defense reduces the FDI error from 23.6–69.9m to 0.49–0.95m and stays within 0.03m of the ideal WLS estimate under equivocating attacks, whereas an adaptive attacker that attempts to evade the threshold causes errors of 3.4–8.5 m. On a six-node Geth Clique network, blockchain-integrated runs reproduce the simulations to within 2 μm, and with a raised block gas limit, an iteration with 1000 submitting UAVs takes about 3.8 s.