Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays

TL;DR

This paper introduces a deep learning approach to emulate large antenna arrays for DOA estimation in MIMO radar, outperforming traditional methods and actual large arrays in certain conditions.

Contribution

It proposes a novel deep learning-based method to reconstruct virtual large array signals, enhancing DOA estimation performance over conventional techniques.

Findings

Deep learning improves DOA estimation accuracy.

The method outperforms traditional MUSIC with actual large arrays.

Optimal training SNR varies with test SNR and angle range.

Abstract

We present a MUSIC-based Direction of Arrival (DOA) estimation strategy using small antenna arrays, via employing deep learning for reconstructing the signals of a virtual large antenna array. Not only does the proposed strategy deliver significantly better performance than simply plugging the incoming signals into MUSIC, but surprisingly, the performance is also better than directly using an actual large antenna array with MUSIC for high angle ranges and low test SNR values. We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.