在存在未知均噪声的情况下,EM和SAGE算法用于DOA估计
1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究适应了预期最大化 (EM) 和空间交替泛化EM (SAGE) 算法,以估计未知噪声的到达方向 (DOA). 对于确定性信号,SAGE算法显示了改进的性能,但对于随机信号并不总是如此.
科学领域:
- 信号处理 信号处理
- 阵列信号处理 阵列信号处理
背景情况:
- 现有的预期最大化 (EM) 和空间交替泛化EM (SAGE) 算法仅限于在已知的噪声环境中对到达方向 (DOA) 的估计.
- 准确的DOA估计在各种应用中至关重要,包括雷达,声纳和无线通信.
研究的目的:
- 扩展EM和SAGE算法用于在未知均噪声中DOA估计.
- 为未知噪声条件提出修改的EM (MEM) 算法.
- 在源功率不均时时增强算法稳定性.
主要方法:
- 该研究适应了现有的EM和SAGE算法,用于在未知均噪声下对DOA估计.
- 引入了一个新的修改EM (MEM) 算法.
- 算法被改进为稳定性与不同的源功率.
主要成果:
- 在EM和MEM算法中,收率相似.
- 在确定性信号模型中,SAGE算法的性能优于EM和MEM.
- 在随机信号模型中,SAGE的表现并不总是优于EM和MEM.
- 具有确定性模型的SAGE对于随机信号需要较少的计算.
结论:
- 开发的算法将DOA估计能力扩展到未知的噪声场景.
- 算法性能因信号模型 (决定性或随机) 和噪声条件而有所不同.
- 计算效率是一个考虑因素,特别是在应用于随机信号的SAGE.
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