DEEP AUGMENTED MUSIC ALGORITHM FOR DATA-DRIVEN DOA ESTIMATION

Deep Augmented MUSIC Algorithm for Data-Driven DoA Estimation

Abstract

Direction of arrival (DoA) estimation is a crucial task in sensor array signal processing, giving rise to various successful model-based (MB) algorithms as well as recently developed data-driven (DD) methods. This paper introduces a new hybrid MB/DD DoA estimation architecture, based on the classical multiple signal classification (MUSIC) algorithm. Our approach augments crucial aspects of the original MUSIC structure with specifically designed neural architectures, allowing it to overcome certain limitations of the purely MB method, such as its inability to successfully localize coherent sources. The deep augmented MUSIC algorithm is shown to outperform its unaltered version with a superior resolution.

Overview

This repository consists of following Python scripts:

• The augMUSIC.py implements the augmented MUSIC algorithm.
• The beamformer.py implements the classic beamforming algorithm.
• The classicMUSIC.py implements the purely model-based MUSIC algorithm.
• The errorMeasures.py defines error measures used to evaluate the DoA estimation algorithms.
• The losses.py script defines custom losses used to train neural augmentations for the MUSIC algorithm.
• The models.py defines neural augmentation architectures for the MUSIC algorithm.
• The plotFigures.py provides visualization of the performances of different DoA algortihms.
• The regularizers.py script defines custom regularizers for the neural augmentations.
• The syntheticEx.py script implements synthetic examples for DoA and combines them to a datase.
• The trainModel.py implements the training of the neural augmentation.
• The utils.py defines some helpful functions.

Requirements