Interpretable and Efficient Beamforming-Based Deep Learning for Single-Snapshot DOA Estimation

Zheng, Ruxin; Sun, Shunqiao; Liu, Hongshan; Chen, Honglei; Li, Jian

doi:10.1109/jsen.2023.3338575

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2023.3338575

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Interpretable and Efficient Beamforming-Based Deep Learning for Single-Snapshot DOA Estimation

Ruxin Zheng,

Shunqiao Sun,

Hongshan Liu

et al.

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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

View full text Add to dashboard Cite

show abstract

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Interpretable and Efficient Beamforming-Based Deep Learning for Single-Snapshot DOA Estimation

Cited by 1 publication

References 30 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

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