基于时空结构的Sparse贝叶斯式学习 - 意识到匹配的场处理
Jia Wang1,2,3, Lanyue Zhang1,2,3, Bo Hu1,2,3
1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
The Journal of the Acoustical Society of America
|January 18, 2024
概括
本研究介绍了一种新的稀疏贝叶斯学习算法,用于水下声源定位. 它通过考虑随时间的连续空间变化来提高准确性,在不需要复杂的运动模型的情况下优于传统方法.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 贝叶斯的推理 贝叶斯的推理
背景情况:
- 传统的匹配场处理 (MFP) 对于水下声源定位的过程,每次都是独立的步骤.
- 这种方法忽略了源随时间的连续空间变化,可能会限制定位准确性.
研究的目的:
- 通过结合时空信息来开发一种改进的水下声源定位算法.
- 通过利用源移动的连续空间结构来提高本地化性能.
主要方法:
- 建议一种稀疏贝叶斯式学习 (SBL) 算法,其中包含一个时空结构意识的先验.
- 在稀疏系数之前的结构捕捉了相邻时间步骤之间的空间连续性.
- 变量贝叶斯推理 (VBI) 用于在一个层次化的贝叶斯框架内推断隐藏的变量.
- 该方法扩展到多频率的情况下,以进一步提高性能.
主要成果:
- 拟议的SBL算法有效地利用了关于连续空间源变化的先前信息.
- 该方法根据邻居信息或独立更新自动选择最佳更新策略.
- 与传统方法相比,定位精度得到了提高.
- 该算法在模拟数据和SWellEx-96实验数据集上都显示出有效性.
结论:
- 拟议的时空结构感知SBL算法为水下声源定位提供了一个强大的方法.
- 它通过整合时间连续性来提高准确性,而不需要预定义的运动模型.
- 该方法显示了对现实世界水下声学应用的巨大潜力.
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