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通过混合卷积图神经网络对稀疏数组进行强大的低快照DOA估计
Hongliang Zhu1, Hongxi Zhao1, Chunshan Bao1
1College of Communications Engineering, Jilin University, Changchun 130015, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种混合卷积图神经网络 (C-GNN),用于准确的到达方向 (DOA) 估计. 该方法在具有有限数据的稀疏传感器阵列中脱而出,在具有挑战性的条件下改进信号处理.
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:
- 在雷达和无线通信等应用中,到达方向 (DOA) 估计至关重要.
- 稀疏的传感器阵列和低快照条件对传统的DOA估计方法构成重大挑战.
- 现有的技术往往因空间采样减少和数据可用性有限而困难.
研究的目的:
- 为在低快照条件下稀疏的传感器阵列开发一个强大的和数据效率高的DOA估计方法.
- 在DOA估计中利用混合深度学习架构来增强特征提取和结构学习.
- 在具有挑战性的现实场景中提高DOA估计的准确性和可靠性.
主要方法:
- 建议采用混合卷积图神经网络 (C-GNN) 架构,集成1D卷积层和图卷积层.
- 差异coarray技术用于将稀疏数组转换为虚拟均线性数组 (VULA),增加自由度.
- 来自数组测量的实值共变矩阵作为输入特征,由MLP回归模块处理用于连续DOA估计.
主要成果:
- C-GNN有效地提取本地空间特征,并从数组数据中学习全球结构依赖.
- 虚拟阵列技术丰富了空间采样,提高了网络捕获信号信息的能力.
- 拟议的方法在噪音较低的快照环境中表现出强大的性能,实现了可靠的DOA估计.
结论:
- 混合C-GNN提供了一种有希望的,数据效率高的方法,用于在稀疏数组中对DOA进行估计,并提供有限的快照.
- 卷积和图形神经网络的集成为利用数组几何和信号特征提供了一个强大的框架.
- 这种方法显示了面临挑战性信号采集约束的实际应用的潜力.
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