Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation

TL;DR

This paper presents a supervised learning algorithm that uses coupled dictionary learning to extrapolate antenna array data, enabling accurate DOA estimation with fewer physical antennas in MIMO radar systems.

Contribution

It introduces a novel antenna extrapolation method that improves DOA resolution using fewer antennas, enhancing MIMO radar capabilities.

Findings

Successfully predicts virtual array signals from reduced antenna data

Resolves multiple targets that are indistinguishable with fewer antennas

Enhances DOA estimation accuracy in MIMO radar systems

Abstract

In this paper, we introduce a novel algorithm that can dramatically reduce the number of antenna elements needed to accurately predict the direction of arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed algorithm predicts the received signal of a large antenna setup using reduced number of antenna by using coupled dictionary learning. Hence, this enables the MIMO radar to resolve more paths, which could not be resolved by the fewer antennas. Specifically, we overcome the problem of inaccurate DOA estimation due to a small virtual array setup. For example, we can use dictionary learning to predict 100 virtual array elements using only 25. To evaluate our algorithm, we used multiple signal classification (MUSIC) as a DOA estimation technique to estimate the DOA for non coherent multiple targets. The results show that using the predicted received signal, the proposed algorithm could resolve all the targets in the scene, which could not been resolved using only the received signal from the reduced antenna setup.