基于多初级假设的FM广播外辐射源雷达网航迹起始算法

胡越洋; 易建新; 万显荣; 程丰; 徐苏霖

doi:10.12000/JR23155

基于多初级假设的FM广播外辐射源雷达网航迹起始算法

DOI: 10.12000/JR23155

武汉大学电子信息学院武汉 430072

基金项目: 国家自然科学基金(61931015, 62071335, 62250024)，湖北省自然科学基金创新群体(2021CFA002)，中央高校自主科研项目(2042022dx0001)

详细信息

作者简介:
胡越洋，博士生，研究方向为雷达数据处理中的目标跟踪和信息融合

易建新，博士，副教授、博士生导师，研究方向为外辐射源雷达信号处理、非线性信号处理

万显荣，博士，教授、博士生导师，研究方向为新体制雷达设计，如外辐射源雷达、高频雷达系统及信号处理

程　丰，博士，副教授，研究方向为雷达信号处理、无线电海洋遥感和雷达软件工程

徐苏霖，博士生，研究方向为FM广播外源雷达信号处理、稀疏雷达信号处理

通讯作者:
易建新 jxyi@whu.edu.cn

责任主编：周建江 Corresponding Editor: ZHOU Jianjiang
中图分类号: TN953
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出版历程
- 收稿日期: 2023-09-01
- 修回日期: 2023-10-30
- 网络出版日期: 2023-11-16
- 刊出日期: 2024-06-28

A Track Initiation Method for FM-based Passive Radar Network Based on Multiple Elementary Hypotheses

School of Electronic Information, Wuhan University, Wuhan 430072, China

Funds: The National Natural Science Foundation of China (61931015, 62071335, 62250024), The Natural Science Foundation of Hubei Province of China (2021CFA002), The Fundamental Research Funds for the Central Universities of China (2042022dx0001)

More Information

Corresponding author: YI Jianxin, jxyi@whu.edu.cn

摘要

摘要: 基于调频(FM)广播信号的外辐射源雷达有着检测概率低、虚警率高、量测精度差的特点，这给组网目标跟踪带来了极大挑战。一方面，较高的虚警率使计算量增加，组网算法的实时性受到考验；另一方面，检测概率低、方位角精度差造成冗余信息缺乏，量测关联与航迹起始变得困难。为解决这些问题，该文提出初级假设点和初级假设航迹的概念，以及基于此概念的FM广播外辐射源雷达网航迹起始算法。首先构造可能的低维关联假设，并解算出与其对应的初级假设点；随后关联不同时刻的初级假设点，形成多条可能的初级假设航迹；最后联合多场雷达网数据进行假设航迹判决，真实目标对应的初级假设航迹会得到确认，错误关联导致的虚假初级假设航迹会被剔除。相比于已有算法，所提算法有着更低的计算量，更快的航迹起始速度，仿真与实测结果均验证了所提算法的有效性。
- FM广播外辐射源雷达 /
- 初级假设 /
- 航迹起始 /
- 雷达网信息融合
Abstract: Passive radars based on FM radio signals have low detection probability, high false alarm rates and poor accuracy, presenting considerable challenges to target tracking in radar networks. Moreover, a high false alarm rate increases the computational burden and puts forward high requirements for the real-time performance of networking algorithms. In addition, low detection probability and poor azimuth accuracy result in a lack of redundant information, making measurement association and track initiation challenging. To address these issues, this paper proposes an FM-based passive radar network based on the concepts of elementary hypothesis points and elementary hypothesis track, as well as a track initiation algorithm. First, we construct possible low-dimensional association hypotheses and solve for their corresponding elementary hypothesis points. Subsequently, we associate elementary hypothesis points from different frames to form multiple possible elementary hypothesis tracks. Finally, by combining multi-frame radar network data for hypothesis track judgment, we confirm the elementary hypothesis tracks corresponding to the real targets, and eliminate the false elementary hypothesis tracks caused by incorrect associations. Result reveal that the proposed algorithm has lower computational complexity and faster track initiation speed than existing algorithms. Moreover, we verified the effectiveness of the proposed algorithm using simulation and experimental results.
- FM-based passive radar /
- Elementary hypothesis /
- Track initiation /
- Information fusion of radar network

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图 1 组网跟踪算法流程图

Figure 1. Flowchart of the proposed tracking algorithm for radar network

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图 2 初级假设点构造示意图

Figure 2. Schematic diagram of constructing elementary hypothesis points

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图 3 所提算法执行过程中间结果

Figure 3. Intermediate results of the proposed algorithm execution process

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图 4 场景1仿真结果

Figure 4. Simulation results of the scenario 1

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图 5 场景1平均航迹数量对比

Figure 5. Comparison of the average number of tracks obtained by different algorithms in scenario 1

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图 6 场景2仿真结果

Figure 6. Simulation results of the scenario 2

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图 7 场景2平均航迹数量对比

Figure 7. Comparison of the average number of tracks obtained by different algorithms in scenario 2

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图 8 外场实验站位分布

Figure 8. Deployment of radar network stations for field experiment

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图 9 实测数据跟踪结果

Figure 9. Tracking results of experimental data

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表 1 仿真目标信息

Table 1. Information of targets in simulation

目标	初始位置(km)	初始速度(m/s)	目标出现场序号	目标终止场序号
1	[150, 130]	[–150, –90]	1	200
2	[80, –100]	[20, –100]	1	200
3	[–150, 100]	[180, –60]	1	200
4	[70, –120]	[100, 100]	1	200
5	[55, 15]	[20, –200]	1	200
6	[–100, 30]	[80, 180]	1	200

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表 2 各收发对不同时刻的检测概率

Table 2. Detection probability of radar stations at different times

收发对序号	场次
收发对序号	1～50	51～100	101～150	151～200
收发对1	0.7	0.7	0.5	0.5
收发对2	0.5	0.8	0.7	0.8
收发对3	0.8	0.5	0.8	0.7

下载: 导出CSV

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