BayesAoA: A Bayesian method for Computation Efficient Angle of Arrival Estimation

TL;DR

This paper introduces BayesAoA, a Bayesian approach for efficient and robust angle of arrival estimation that converges faster, requires less computation, and adapts online to changing channel conditions.

Contribution

It presents a novel Bayesian method for AoA estimation that is less sensitive to initialization, computationally efficient, and capable of online adaptation using a Hedge type solution.

Findings

Achieves 92% accuracy in low noise conditions.

Uses only 19.3% of the computation of brute-force methods.

Faster convergence compared to traditional methods.

Abstract

The angle of Arrival (AoA) estimation is of great interest in modern communication systems. Traditional maximum likelihood-based iterative algorithms are sensitive to initialization and cannot be used online. We propose a Bayesian method to find AoA that is insensitive towards initialization. The proposed method is less complex and needs fewer computing resources than traditional deep learning-based methods. It has a faster convergence than the brute-force methods. Further, a Hedge type solution is proposed that helps to deploy the method online to handle the situations where the channel noise and antenna configuration in the receiver change over time. The proposed method achieves $92\%$ accuracy in a channel of noise variance $10^{-6}$ with $19.3\%$ of the brute-force method's computation.