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针对异步多传感器网络的多目标跟踪AA融合方法

Kuiwu Wang^1,2, Qin Zhang¹, Guimei Zheng¹

¹School of Air Defense and Missile Defense, Air Force Engineering University, Xi'an 710051, China.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

本研究介绍了一种优化的传感器网络,用于异步多目标跟踪. 这种新的方法通过自适应地选择传感器节点进行数据融合来提高跟踪精度.

科学领域:

传感器网络传感器网络
目标追踪目标追踪
数据融合数据融合

背景情况:

来自多个传感器的异步数据使多个目标的跟踪变得复杂.
现有的方法在为核聚变选择最佳传感器方面存在困难.

研究的目的:

开发一个优化的传感器网络,用于异步多目标跟踪.
通过适应性传感器节点选择和融合来提高跟踪精度.

主要方法:

在每个传感器节点使用的概率假设密度 (PHD) 过器.
开发了一种用于数据融合的复合测量信息方法.
为了追踪错误,衍生出贝叶斯式克拉梅尔-拉奥下界.
采用顺序二次编程 (SQP) 进行传感器节点选择.

主要成果:

拟议的AA聚变优化模型有效地减少了跟踪错误.
适应性传感器节点选择显著提高了异步多目标跟踪精度.
与现有算法相比,模拟显示出更高的性能.

结论:

开发的方法为多传感器异步多目标跟踪提供了更高的精度.
适应性传感器选择对于优化聚变性能至关重要.
这种方法为复杂的跟踪场景提供了强大的解决方案.

关键词:

这是一个PHD过器.核聚变的算术平均值不同步的多目标追踪.多传感器网络多传感器网络随机有限集合随机有限集合

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