基于互信息熵-改进PHD协同的非合作双基地雷达目标跟踪

潘嘉蒙; 李纯; 郑曦楠; 陈健; 鲍庆龙

doi:10.12000/JR25118

基于互信息熵-改进PHD协同的非合作双基地雷达目标跟踪

DOI: 10.12000/JR25118 CSTR: 32380.14.JR25118

国防科技大学电子科学学院长沙 410073

基金项目: 国家自然科学基金(62201594, 62201588, 62501610)

详细信息

作者简介:
潘嘉蒙，博士，讲师，主要研究方向为无源探测、目标跟踪和雷达信号处理

李　纯，博士生，主要研究方向为雷达目标跟踪

郑曦楠，博士生，主要研究方向为雷达信号处理与波形设计

陈　健，博士，讲师，主要研究方向为雷达抗干扰与信号处理

鲍庆龙，博士，副教授，主要研究方向为雷达数据采集、无源探测和雷达信号处理

通讯作者:
陈健chenjian13a@nudt.edu.cn

鲍庆龙 baoqinglong@nudt.edu.cn

责任主编：万显荣 Corresponding Editor: WAN Xianrong
中图分类号: TN953
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出版历程
- 收稿日期: 2025-07-01

Target Tracking for Passive Bistatic Radar Based on Mutual Information Entropy and Improved PHD

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62201594, 62201588, 62501610)

More Information

Corresponding author: CHEN Jian, chenjian13a@nudt.edu.cn; BAO Qinglong, baoqinglong@nudt.edu.cn

摘要

摘要: 针对非合作双基地雷达目标跟踪时主要面临的高杂波率、低检测概率等问题，该文提出了一种基于互信息熵和改进PHD滤波器的目标跟踪协同处理框架，首先将目标点和杂波点与参考模型间不同的统计相关程度量化为互信息熵值，基于互信息熵维特征完成杂波点迹筛除；其次通过动态权值补偿对经典PHD滤波器进行改进，减缓粒子权值归零过程的同时减少目标误删现象，解决低检测概率下点迹不连续且间隔随机给目标跟踪带来的点迹断联、目标丢失等问题。通过仿真实验验证了所提算法框架的有效性与性能，外场实测数据验证了所提方法在实际应用中可取得良好的目标跟踪结果。
- 非合作双基地雷达 /
- 低检测概率 /
- 目标跟踪 /
- 互信息熵 /
- PHD滤波
Abstract: This study proposes a processing framework based on mutual information entropy and an improved probability hypothesis density filter to address the key challenges—high clutter density and low detection probability—in passive bistatic radar target tracking. First, statistical differences in the correlation between target and clutter points, as well as between reference models, are quantified as mutual information entropy values, which are then used to eliminate clutter points. Second, the classical probability hypothesis density filter is improved through dynamic weight compensation, mitigating particle weight degeneration and reducing the deletion of false targets. This approach effectively resolves issues such as track fragmentation and target loss caused by discontinuous measurements with random intervals under low detection probability. The effectiveness of the proposed framework was verified through simulation experiments, and field test data demonstrated that the proposed method achieves good target-tracking performance in practical applications.
- Passive Bistatic Radar (PBR) /
- Low detection probability /
- Target tracking /
- Mutual information entropy /
- PHD filter

HTML全文

图 1 双基地雷达定位结构示意图

Figure 1. Schematic diagram of the Bistatic radar positioning structure

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图 2 熵之间的关系图

Figure 2. Relationship between entropies

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图 3 不同采样方法下算法的蒙特卡罗实验结果

Figure 3. Monte Carlo experimental results of the algorithm under different sampling methods

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图 4 动态权值补偿机制改进前后对比

Figure 4. Comparison before and after improvement of the dynamic weight compensation mechanism

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图 5 MIE-IPHD算法整体流程

Figure 5. Overall Flow of the MIE-IPHD Algorithm

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图 6 目标航迹及量测点迹

Figure 6. Targets’ trajectories and measurements

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图 7 点迹互信息熵显示

Figure 7. MIE of the trace points

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图 8 MIE-IPHD算法在不同检测概率下的跟踪结果

Figure 8. Tracking results of the MIE-IPHD algorithm under different low detection probabilities

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图 9 MIE-IPHD算法在不同杂波率下的跟踪结果

Figure 9. Tracking results of the MIE-IPHD algorithm under different clutter rates

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图 10 多目标跟踪误差结果对比

Figure 10. Multi-target tracking error comparison

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图 11 非合作双基地雷达实测数据

Figure 11. Measured data of passive bistatic radar

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图 12 实测数据目标跟踪结果对比

Figure 12. Comparison of target tracking results based on measured data

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表 1 仿真目标参数设置

Table 1. Simulation parameter setting of target

目标	起始位置(x,y)/ m	速度($ {v}_{x},{v}_{y} $)/ m/s	存活时间/ s
1	(0, 2700)	(80, 80)	(0, 80)
2	(3000, 0)	(60, 100)	(0, 90)
3	(0, 9000)	(70, -80)	(10, 100)

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表 2 仿真场景参数设置

Table 2. Simulation parameter setting of scenario

双基地基线距离	10 km
探测距离范围	[10 km, 60 km]
探测角度范围	[30°, 150°]
目标检测概率$ {P}_{d} $	0.5
泊松杂波均值$ \lambda $	20
距离量测误差(精度)	30 m
角度量测误差(精度)	1°

下载: 导出CSV

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