# Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation

## TL;DR

This paper demonstrates that a well-designed deep neural network can estimate angles of arrival with maximum likelihood performance and lower complexity, outperforming traditional signal processing methods across different noise levels and inaccuracies.

## Contribution

It introduces a DNN-based approach for AOA estimation that achieves maximum likelihood performance with feasible complexity, surpassing existing methods.

## Key findings

- DNN attains maximum likelihood performance.
- DNN outperforms traditional algorithms across SNRs.
- DNN is robust to array response inaccuracies.

## Abstract

The problem of estimating the number of sources and their angles of arrival from a single antenna array observation has been an active area of research in the signal processing community for the last few decades. When the number of sources is large, the maximum likelihood estimator is intractable due to its very high complexity, and therefore alternative signal processing methods have been developed with some performance loss. In this paper, we apply a deep neural network (DNN) approach to the problem and analyze its advantages with respect to signal processing algorithms. We show that an appropriate designed network can attain the maximum likelihood performance with feasible complexity and outperform other feasible signal processing estimation methods over various signal to noise ratios and array response inaccuracies.