On The Security of AoA Estimation
Amr Abdelaziz, C. Emre Koksal, Hesham El Gamal

TL;DR
This paper investigates the security vulnerabilities of AoA estimation under jamming in Rician channels, deriving optimal strategies for both jammer and receiver, and demonstrating the robustness of ML estimators against such attacks.
Contribution
It introduces a comprehensive analysis of AoA estimation security, deriving optimal jamming and estimation strategies, and highlights the robustness of ML estimators in hostile environments.
Findings
Optimal jammer strategy mimics training sequence.
ML AoA estimator remains optimal under jamming.
Jammer's power allocation critically affects estimation accuracy.
Abstract
Angle of Arrival (AoA) estimation has found its way to a wide range of applications. Much attention have been paid to study different techniques for AoA estimation and its applications for jamming suppression, however, security vulnerability issues of AoA estimation itself under hostile activity have not been paid the same attention. In this paper, the problem of AoA estimation in Rician flat fading channel under jamming condition is investigated. We consider the scenario in which a receiver with multiple antenna is trying to estimate the AoA of the specular line of sight (LOS) component of signal received from a given single antenna transmitter using a predefined training sequence. A jammer equipped with multiple antennas is trying to interrupt the AoA estimation phase by sending an arbitrary signal. We derive the optimal jammer and receiver strategies in various scenarios based on the…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Radar Systems and Signal Processing · Direction-of-Arrival Estimation Techniques
