A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling

Abstract

In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of active sources is not smaller than the number of simultaneously sampled antenna elements. For this purpose, we propose new schemes based on neural networks and estimators that combine neural networks with gradient steps on the likelihood function. These methods are able to outperform existing estimators in terms of mean squared error and model selection accuracy, especially in the low snapshot domain, at a drastically lower computational complexity.

arXiv commentAccepted Version

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
• DeepMUSIC: Multiple Signal Classification via Deep Learning
• Source localization in an ocean waveguide using supervised machine learning
• Model-aided Deep Neural Network for Source Number Detection
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Cited by in corpus (8)

• Ziv-Zakai Bound for DOAs Estimation
• DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
• Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm
• Audio scene monitoring using redundant ad-hoc microphone array networks
• Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
• Wireless Propagation Parameter Estimation with Convolutional Neural Networks
• Model Order Selection with Variational Autoencoding
• ChainNet: Neural Network-Based Successive Spectral Analysis