在非统一的杂乱中用于被动雷达的RCS-多普勒辅助MM-GM-PHD过器
Jia Wang1, Baoxiong Xu1, Zhenkai Zhang1
1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
一个新的特征匹配算法通过减少混乱引起的虚假目标来改善被动雷达跟踪. 这提高了追踪精度和目标计数,特别是在具有挑战性的环境中.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 雷达系统 雷达系统
背景情况:
- 多模型概率假设密度 (MM-PHD) 过器在被动雷达中有效用于多目标跟踪.
- 被动雷达系统中不均的杂乱导致误估和错误目标,降低了跟踪性能.
- 现有的MM-PHD过器在混乱的环境和低检测概率中难以准确.
研究的目的:
- 提出一种用于被动雷达跟踪的新型特征匹配算法,以解决虚假目标的问题.
- 提高被动雷达系统中多目标追踪的稳定性和准确性,特别是在杂乱状况不均的情况下.
- 改进枢机值估计,减少被动雷达跟踪中的杂乱干扰.
主要方法:
- 开发了一个特征匹配的多模型概率假设密度 (FM-MM-GM-PHD) 算法.
- 使用目标雷达截面 (RCS) 和多普勒特征,完善了测量概率函数.
- 集成的自适应组件修剪,吸收和错过报警纠正机制.
主要成果:
- 提出的FM-MM-GM-PHD算法显著超过了传统的MM-PHD过器.
- 在跟踪精度和核心值估计方面表现出卓越的性能.
- 在不均的杂乱环境和低检测概率下表现出特别高的有效性.
结论:
- FM-MM-GM-PHD算法有效地抑制虚假目标,并减少被动雷达中的杂乱干扰.
- 增强的跟踪精度和核心值估计为被动雷达应用提供了显著的改进.
- 该算法提供了一个强大的解决方案,用于在具有挑战性的现实场景中多动作目标跟踪.
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