变化贝叶斯算法用于在传感器网络中通过非线性测量操纵目标跟踪
Yumei Hu1, Quan Pan2,3, Bao Deng1
1Xi'an Aeronautics Computing Technique Research Institute, AVIC, Xi'an 710069, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种分布式融合变异贝叶斯卡尔曼波器,用于在网络传感器中使用多普勒测量跟踪机动目标. 与传统方法相比,这种新的方法在复杂场景中提高了估计准确度.
科学领域:
- 信号处理 信号处理
- 估计理论 估计理论
- 网络化系统 网络化系统
背景情况:
- 变量贝叶斯方法对于非线性估计问题至关重要,但它们的性能对线性近似和系统非线性敏感.
- 在联网传感器系统中使用多普勒测量来操纵目标跟踪,这带来了重大的估计挑战.
研究的目的:
- 提出一个分布式聚变贝叶斯卡尔曼波器用于网络机动目标跟踪.
- 用证据下界和后部克拉梅尔-拉奥下界来评估拟议过器的性能.
主要方法:
- 使用自然梯度和同时扰动随机方法实现一个变化的贝叶斯卡尔曼波器.
- 在单跳约束下,开发分布式聚变方法用于网络传感器数据.
- 分析理论界限,包括证据下限和后部克拉梅尔-拉奥下限.
主要成果:
- 拟议的分布式聚变过器在操纵目标跟踪方面表现出更好的性能.
- 模拟表明,分布式方法在后方克拉梅尔-拉奥下限和根-平均-平方误差方面优于集中融合.
- 3σ结合分析进一步验证了拟议方法的增强准确性.
结论:
- 分布式聚变贝叶斯卡尔曼波器是网络机动目标跟踪的有效解决方案.
- 提出的方法为复杂,非线性和网络场景提供了强大而准确的估计框架.
- 这项工作推进了传感器网络分布式估计的最新技术.
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