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Updated: Sep 9, 2025

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基于离网稀疏贝叶斯学习的任意数组的快速解卷束形成
Jianli Huang1, Yu Wang1, Zaixiao Gong1
1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, Chinahuangjianli@mail.ioa.ac.cn, wy@mail.ioa.ac.cn, gzx@mail.ioa.ac.cn, nhq@mail.ioa.ac.cn, wangj@mail.ioa.ac.cn, whb@mail.ioa.ac.cn.
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|August 28, 2025
概括
这项研究引入了离网稀疏贝叶斯式学习,用于解卷束形成,增强现实目标的空间分辨率. 改进的方法克服了传统技术的转移变量束图案的局限性,并针对采样网.
科学领域:
- 信号处理
- 阵列信号处理
- 计算电磁学
背景情况:
- 解卷束成形 (dCv) 可以提高空间分辨率而不会增加阵列大小.
- 传统的dCv与转移变量的光束模式扎,并且目标不在采样网上.
- 精确的空间定位在各种传感应用中至关重要.
研究的目的:
- 将离网稀疏贝叶斯式学习 (OGSBL) 扩展到解卷束形成 (dCv).
- 解决dCv关于转移变量光束模式和离网目标的限制.
- 提高光束成形技术的空间分辨率和精度.
主要方法:
- 每个角度包含光束图案的通用卷积模型.
- 在粗格上对采样位置进行参数化,以减少建模错误.
- 控制输出光束数量以覆盖感兴趣的空间区域以实现更快的融合.
主要成果:
- 拟议的OGSBL增强的dCv有效处理转移变量的光束模式.
- 目标的精确定位不是在采样网上实现的.
- 模拟结果证明了该方法的良好性能和准确性.
结论:
- 将OGSBL与dCv集成为增强空间分辨率提供了强大的解决方案.
- 这种方法克服了传统dCv的主要局限性.
- 该方法显示了高级束形应用的巨大潜力.
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