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使用区块分散贝叶斯式学习与扩展字典来估计空间分布源的到达方向
Anbang Zhao1,2,3, Keren Wang1, Juan Hui1,2,3
1College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China.
The Journal of the Acoustical Society of America
|March 12, 2024
概括
本研究提出了一种新的稀疏贝叶斯方法,用于估计空间分布源的到达方向 (DOA). 该方法增强了能量聚焦,并实现了精确的DOA确定,即使数据有限,信号噪声比低.
科学领域:
- 阵列信号处理系统的信号处理.
- 统计信号处理 统计信号处理
- 电磁学 电磁学 电磁学 电磁学
背景情况:
- 估计空间分布源的到达方向 (DOA) 是数组信号处理中的一个复杂问题.
- 现有的方法通常在有限的快照和低信号对噪声比率 (SNR) 下难以准确.
研究的目的:
- 引入一种有效的稀疏贝叶斯法,用于对空间分布源的DOA估计.
- 为了提高在具有挑战性的信号条件下DOA确定中的能量聚焦和精度.
主要方法:
- 使用多维的Slepian信号子空间建模空间分布源.
- 采用区块分散贝叶斯式学习来进行参数估计.
- 在多快照区块散射框架内导出一个复杂的高斯后.
- 使用预期最大化算法进行超参数估计.
主要成果:
- 拟议的方法证明了对空间传播信号的优越能量聚焦.
- 即使使用有限的快照和低SNRs,也可以实现准确的DOA确定.
- 通过数值模拟和现实世界海试数据进行验证.
结论:
- 开发的稀疏贝叶斯框架为空间分布源的DOA估计提供了强大的解决方案.
- 该方法在具有有限数据和噪声特征的具有挑战性的环境中提供了显著的优势.
- 这种技术增强了阵列信号处理的功能,用于复杂的源本地化.
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