Single Snapshot DoA Estimation from a Rotman Lens using Machine Learning Techniques

Weis, Matthias; Köhler, Michael; Saam, Alexander; Worms, Josef

doi:10.23919/irs48640.2020.9253832

2020 21st International Radar Symposium (IRS) 2020

DOI: 10.23919/irs48640.2020.9253832

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Single Snapshot DoA Estimation from a Rotman Lens using Machine Learning Techniques

Matthias Weis

Michael Köhler

Alexander Saam

et al.

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Cited by 2 publications

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An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

Zhang,

He,

Liu

et al. 2024

Sci Rep

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This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions.

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An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

Zhang,

He,

Liu

et al. 2024

Sci Rep

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Liquid-Crystal-Driven Reconfigurable Intelligent Surface With Cognitive Sensors for Self-Sustainable Operation

Youn,

An,

Kim

et al. 2023

IEEE Trans. Antennas Propagat.

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Single Snapshot DoA Estimation from a Rotman Lens using Machine Learning Techniques

Cited by 2 publications

References 12 publications

An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

Liquid-Crystal-Driven Reconfigurable Intelligent Surface With Cognitive Sensors for Self-Sustainable Operation

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