DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
Andreas Barthelme, Wolfgang Utschick
Abstract
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.
Topics & Concepts
Covariance matrixEstimatorComputer scienceEstimation of covariance matricesCovarianceDirection of arrivalAlgorithmArtificial neural networkMatrix (chemical analysis)Covariance intersectionPattern recognition (psychology)Artificial intelligenceMathematicsStatisticsTelecommunicationsComposite materialAntenna (radio)Materials scienceDirection-of-Arrival Estimation TechniquesSpeech and Audio ProcessingIndoor and Outdoor Localization Technologies