An Efficient Convolutional Neural Network with Supervised Contrastive Learning for Multi-Target DOA Estimation in Low SNR
An Efficient Convolutional Neural Network with Supervised Contrastive Learning for Multi-Target DOA Estimation in Low SNR
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DOI：
10.3390/axioms12090862
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发表时间：
2023-09
期刊：
影响因子：
2
通讯作者：
Y. Li;Zhengjie Zhou;Cheng Chen;Peng Wu;Zhiquan Zhou
中科院分区：
文献类型：
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作者：
Y. Li;Zhengjie Zhou;Cheng Chen;Peng Wu;Zhiquan Zhou
本文提出了一种改进的高效卷积神经网络（CNN）和一种新的有监督对比学习（SCL）方法，用于均匀线阵低信噪比（SNR）条件下多目标的波达方向（DOA）估计。该模型使用网格上的设置进行训练，因此该问题被建模为多标签分类任务。仿真结果表明，该方法在低信噪比和少量快拍情况下具有较好的鲁棒性。值得注意的是，该方法表现出很强的能力，在检测源的数量，同时估计他们的DOA。此外，与传统的CNN方法相比，我们改进的高效CNN将参数数量显著减少了16倍，同时仍然获得了相当的结果。通过潜在空间的可视化和先进的特征学习理论分析了该方法的有效性。
In this paper, a modified high-efficiency Convolutional Neural Network (CNN) with a novel Supervised Contrastive Learning (SCL) approach is introduced to estimate direction-of-arrival (DOA) of multiple targets in low signal-to-noise ratio (SNR) regimes with uniform linear arrays (ULA). The model is trained using an on-grid setting, and thus the problem is modeled as a multi-label classification task. Simulation results demonstrate the robustness of the proposed approach in scenarios with low SNR and a small number of snapshots. Notably, the method exhibits strong capability in detecting the number of sources while estimating their DOAs. Furthermore, compared to traditional CNN methods, our refined efficient CNN significantly reduces the number of parameters by a factor of sixteen while still achieving comparable results. The effectiveness of the proposed method is analyzed through the visualization of latent space and through the advanced theory of feature learning.