基于图形神经网络的随机稀疏线性数组的到达方向估计
Yiye Yang1, Miao Zhang1, Shihua Peng1
1School of Electronic Science and Technology, Xiamen University, Xiamen 361005, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
一个新的图形神经网络 (GNN) 算法改善了稀疏数组的到达方向 (DOA) 估计. 这种方法在具有挑战性的条件下提高了准确性,例如低信号噪声比和有限的数据,优于传统技术.
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:
- 传统的到达方向 (DOA) 估计算法与不统一和稀疏的线性数组相斗争.
- 现有的深度学习模型往往无法在复杂的数组配置中有效捕获空间信息.
研究的目的:
- 为DOA估计提出一种基于图形神经网络 (GNN) 的新型算法.
- 为了解决处理随机稀疏线性数组时常规方法的局限性.
- 在具有挑战性的环境条件下实现可靠的DOA估计.
主要方法:
- 开发一种利用邻近节点聚合和更新操作的GNN模型.
- 为减少网络复杂性,GNN的端到端培训.
- 在不同的信号噪声比率 (SNR) 和快照计数中对均和稀疏的线性数组进行比较实验.
主要成果:
- 在极为稀疏的数组上,GNN模型展示了优越的角度估计性能.
- 在准确性方面超过传统算法和现有的深度学习模型 (CNN,FC).
- 通过有限的快照,低的SNR和大阵列稀疏度实现了出色的DOA估计.
结论:
- 拟议的GNN算法为稀疏数组的DOA估计提供了显著的进步.
- 它提供了一个低延迟,计算效率高的解决方案,适合实时应用.
- 该方法即使在传统算法失败的复杂条件下也有效.
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