一种修改后的强大的多目标追踪方法,具有高杂乱和水下非静止测量噪声 (sea)
Xianghao Hou1,2,3, Yuxuan Chen1,2,3, Weisi Hua1,2,3
1The School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.
The Journal of the Acoustical Society of America
|February 2, 2026
概括
这项研究引入了一种新的算法,用于在具有挑战性的水下条件下进行强大的多目标跟踪. 该方法通过改善目标杂乱区分和自适应性纠正测量噪声来提高准确性.
科学领域:
- 水下声学 水下声学
- 信号和信息处理.
- 多目标追踪追踪多目标追踪
背景情况:
- 对多个水下目标的稳健跟踪至关重要,但由于杂乱和非静止噪音,这是一项挑战.
- 现有的随机有限集 (RFS) 算法在这种环境中扎着观察概率污染和精度差.
研究的目的:
- 提出一个强大的两阶段修改变量贝叶斯三角形概括标记多伯诺利多目标跟踪算法.
- 为了提高跟踪精度和稳定性,在杂乱的水下环境中与非静止测量噪声.
主要方法:
- 引入了Sage-Husa (SH) 估计用于初始测量噪声协差校正.
- 采用变量贝叶斯框架来进一步优化测量噪声共变率估计.
- 采用了多个伯诺利过的更新阶段,标记为delta-generalized.
主要成果:
- 在自适应式RFS中有效地减轻了概率函数的杂乱污染.
- 在复杂的噪音条件下,提高了目标和杂乱之间的区别.
- 通过实时噪声校正,显著提高了水下多目标主动跟踪的稳定性.
结论:
- 拟议的算法显著优于传统和现有的适应性通用标记的多伯诺利方法.
- 在模拟和实验中表现出优异的性能,具有强大的杂乱和非静止噪声.
- 为水下多目标主动跟踪提供了强大的解决方案.
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