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相关概念视频

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稀有贝叶斯式学习与伯努利-高斯式 priors 离网匹配场处理.

Qingji Li1,2,3, Xiao Han1,2,3, Ran Cao1,2,3

  • 1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.

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|September 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于匹配场处理 (MFP) 的网格适应模型,通过优化网格节点来提高源定位精度. 新的离网算法提高了本地化成功率和侧叶抑制的性能.

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科学领域:

  • 信号处理 信号处理
  • 阵列处理 阵列处理.
  • 压缩感应 压缩感应

背景情况:

  • 传统的匹配场处理 (MFP) 由于离散网格计算而遭受基础不匹配错误.
  • 现有的基准不匹配缓解方法是计算密集的或需要特定的函数形式,限制其应用到MFP.

研究的目的:

  • 开发一个适应电网的模型,以减轻MFP的基础不匹配.
  • 提出一个离网稀疏的贝叶斯学习算法,用于精确的源本地化.

主要方法:

  • 在网格适应模型中对网格节点进行本地化优化.
  • 使用变化期望-最大化开发一个离网的伯努利-高斯稀疏贝叶斯学习算法.
  • 重构网格调整作为边界受约束的线性最小平方优化.

主要成果:

  • 拟议的方法有效地克服了离网源定位的电网约束.
  • 纳入伯努利-高斯先验增强了没有事先信息的稀疏性.
  • 与传统方法相比,在本地化成功率和侧叶抑制方面表现卓越.

结论:

  • 电网适应模型和离网算法通过解决基础不匹配,提供精确的源本地化.
  • 与传统的巴特莱特和稀疏的贝叶斯学习处理器相比,该方法提供了显著的改进.
  • 通过数值模拟和SwellEX-96实验数据进行验证.