在低SNR环境中基于深度学习的DOA估计的GAN-CNN融合框架
Zhenshan Zhang1, Wenjie Xu1, Haitao Zou1
1School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究引入了一种使用生成对抗网络 (GAN) 和卷积神经网络 (CNN) 的新框架,以改善低信号对噪声比 (SNR) 环境中的到达方向 (DOA) 估计. 该方法显著提高了准确性和稳定性,即使数据有限.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 阵列信号处理 阵列信号处理
背景情况:
- 在低信号对噪声比 (SNR) 条件下,到达方向 (DOA) 估计性能显著下降.
- 传统的算法和深度学习模型在低SNR场景中与受损的空间信息和有限的训练数据作斗争.
研究的目的:
- 在具有低SNR和数据稀缺的环境中,开发一个新的两阶段框架,用于在具有挑战性的低SNR和数据稀缺环境中进行强大的DOA估计.
- 为了提高信号质量,并提取强大的空间特征,以提高DOA精度.
主要方法:
- 一个两阶段的框架,整合了用于信号增强的生成对抗网络 (GAN) 和用于DOA估计的复杂值卷积神经网络 (CNN).
- GAN使用注意力机制和相一致损失函数来减少噪声,同时保持空间相位.
- 增强信号被转换为共变矩阵,并由复杂值CNN处理以提取特征.
主要成果:
- 实现了72.2%的DOA准确度和3.9°根平均平方误差 (RMSE) 在-10dB SNR与500快照.
- 远远超过了传统和深度学习的基线方法.
- 在数据稀缺的条件下,仅使用50张快照,以93.8%的准确率表现出强大的稳定性.
结论:
- 拟议的框架为在低SNR和数据有限的场景中可靠的DOA估计提供了实用和有效的解决方案.
- 基于GAN的信号增强和复杂值CNN的集成显著提高了DOA估计性能.
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