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Deep Learning-Based DOA Estimation
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AI
DOI：10.1109/TCCN.2024.3360527.png)
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Abstract
En 中文
Direction-of-arrival (DOA) estimation is a vital research topic in array signal processing, with extensive applications in many fields. In recent years, deep learning has been applied to DOA estimation to improve the performance. However, most existing deep learning-based DOA estimation methods extract DOA information from the covariance matrix (CM) input. In this paper, we introduce a novel deep learning-based DOA estimation scheme that utilizes the raw in-phase (I) and quadrature (Q) components of the signal as the input. We formulate the problem as single-label classification and multi-label classification based on the number of signal sources. We design a convolutional neural network to solve the problems and to adapt to different number of snapshots. We also propose a deep learning regression-based method to overcome the limitations of classify-based methods in dealing with off-grid angles. We conduct extensive experiments with simulations and over-the-air collected signals to analyze the performance of the proposed method in various scenarios including different SNRs, additive generalized Gaussian noise (AGGN) and extreme multi-source DOA estimation. Results demonstrate that our proposed method outperforms the existing deep learning-based DOA estimation methods.
Keywords:
Direction-of-arrival estimation
Estimation
Covariance matrices
Adaptation models
Training
Convolutional neural networks
Antenna arrays
Direction-of-arrival (DOA) estimation
deep learning
residual neural network
classification
regression
Journal
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7
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1.6K
Citations:
5.5K
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