离网水下声源到达方向估计方法基于代实证模式分解间隔值
Chuanxi Xing1,2, Guangzhi Tan1,2, Saimeng Dong1,2
1College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一种新的算法来估计到达方向 (DOA),使用代EMD间隔值 (EMD-IIT) 和离网稀疏贝叶斯学习来提高噪音浅海的准确性.
科学领域:
- 信号处理 信号处理
- 海洋声学 海洋声学
- 阵列信号处理 阵列信号处理
背景情况:
- 浅海环境对水音机阵列来说,会带来巨大的海洋噪音挑战.
- 这种噪音降低了目标到达方向 (DOA) 估计的准确性和分辨率.
- 现有的算法与噪声干扰作斗争,影响性能.
研究的目的:
- 为浅海开发一个强大的DOA估计算法.
- 在噪音条件下克服传统方法的局限性.
- 提高目标定向估计的准确性和分辨率.
主要方法:
- 提出了一种新的算法,将代EMD间隔值 (EMD-IIT) 结合起来,用于DOA估计的denoising和离网稀疏贝叶斯学习.
- 将单数值分解应用于已删除的信号,以建立一个离网稀疏重建模型.
- 利用贝叶斯式学习,通过最大化目标信号的后期概率来推导DOA估计.
主要成果:
- 在相邻的信号源之间的8度亚齐图斯距离下实现了100%的分辨率概率.
- 保持了100%的分辨率概率,即使在信号噪声比低的-9dB的情况下.
- 在本地化准确性,运行时间和稳定性方面,在传统的MUSIC-like和OGSBI-SVD算法上表现出卓越的性能.
结论:
- 拟议的EMD-IIT和离网稀疏贝叶斯学习算法有效地减轻了浅海的海洋噪音干扰.
- 与现有方法相比,该算法显著提高了DOA估计的准确性和分辨率.
- 它为水下目标定位估计提供了更高的稳定性和效率.
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