Quantum mechanics

Algorithm

Statistics

Covariance matrix

Covariance intersection

Materials science

Eigenvalues and eigenvectors

Telecommunications

Computer science

Antenna (radio)

Estimation of covariance matrices

Covariance

Matrix (chemical analysis)

Physics

Pattern recognition (psychology)

Mathematics

Estimator

Artificial intelligence

Composite material

Direction of arrival

Artificial neural network

Matrix algebra

IEEE Signal Processing Letters

In this paper, we discuss a new approach to direction of arrival estimation for systems with subarray sampling. We propose to estimate the covariance matrix of the full array from the sample covariance matrices of the subarrays using a neural network. This technique enables the estimation of more sources than radio frequency chains by applying a MUSIC estimator to the reconstructed full covariance matrix. The proposed method is able to outperform classical estimators and has some benefits compared to a recently proposed machine learning-based technique for these systems, which models the direction of arrival estimation problem as a end-to-end regression task.

DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction