Robust Vehicle Localization Based on Adaptive DOA Estimation and UKF under Varying Interference Conditions

Accurate localization in GPS-denied environments is vital for ITS and vehicular networks. While Root-MUSIC offers high accuracy in clean LoS conditions, FBSS enhances robustness under multipath and interference. To overcome the limitations of fixed estimators, this paper proposes an adaptive DOA-based localization framework that selects Root-MUSIC+UKF in low-interference regimes and FBSS-Root-MUSIC+UKF when interference is strong. Simulations show that the proposed method consistently reduces positioning error across varying SNR and interference levels, providing reliable GPS-free vehicle localization in non-stationary wireless environments.