结合幅度信息的扩展目标随机有限集跟踪方法

柳超; 孙进平; 陈小龙; 张志国

doi:10.12000/JR19071

结合幅度信息的扩展目标随机有限集跟踪方法

DOI: 10.12000/JR19071

柳超^{1, 2,},
孙进平^1, ,,
陈小龙^3,,
张志国^1,

1.
北京航空航天大学电子信息工程学院北京 100191
2.
海军92853部队葫芦岛 125106
3.
海军航空大学烟台 264001

基金项目: 国家自然科学基金(61471019, U1633122)

详细信息

作者简介:
柳　超(1984–)，男，山东宁阳人。北京航空航天大学博士生，研究方向为雷达数据处理。E-mail: LC2016@buaa.edu.cn

孙进平(1975–)，男，甘肃天水人，北京航空航天大学教授，博士生导师，主要研究方向为高分辨率雷达信号处理，数据处理，稀疏微波成像。E-mail: sunjinping@buaa.edu.cn

陈小龙(1985–)，男，山东烟台人，海军航空大学副教授，主要研究方向为雷达动目标检测、海杂波抑制、雷达信号精细化处理等。E-mail: cxlcxl1203@163.com

张志国(1995–)，男，山东聊城人，北京航空航天大学博士生，研究方向为雷达数据处理。E-mail: zzguo2016@163.com

通讯作者:
孙进平 sunjinping@buaa.edu.cn

责任主编：何子述 Corresponding Editor: HE Zishu
中图分类号: TP391.41
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出版历程
- 收稿日期: 2019-07-25
- 修回日期: 2019-10-26
- 网络出版日期: 2020-08-28

Random Finite Set-based Extended Target Tracking Method with Amplitude Information

LIU Chao^{1, 2
,},
SUN Jinping^{1
, ,},
CHEN Xiaolong^3
,,
ZHANG Zhiguo^1
,

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2.
PLA 92853 Unit, Huludao 125106, China
3.
Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (61471019, U1633122)

More Information

Corresponding author: SUN Jinping, sunjinping@buaa.edu.cn

摘要

摘要: 基于随机有限集的扩展目标跟踪方法通常根据量测的空间信息进行量测划分，在杂波密集环境下有可能将杂波量测划入目标单元，从而造成跟踪性能的下降。为此，该文将目标和杂波的幅度信息引入高斯逆威沙特概率假设密度(GIW-PHD)滤波器，通过计算量测子集的幅度似然寻找最优的量测划分方法。此外，计算量测单元的中心时，采用幅度加权的方法计算量测单元的质量中心，以取代目前广泛使用的几何中心，从而进一步降低杂波对滤波器的干扰。在信杂比分别为13 dB和6 dB的条件下，通过对Rayleigh杂波中Swerling 1型起伏目标的跟踪结果证明了所提方法相比高斯逆威沙特概率假设密度滤波器具有更优的势估计和状态估计性能。
- 扩展目标跟踪 /
- 随机有限集 /
- 幅度信息 /
- 高斯逆威沙特概率假设密度滤波器
Abstract: The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter.
- Extended target tracking /
- Random finite set /
- Amplitude information /
- Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter

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图 1 扩展目标量测

Figure 1. Measurements of the extended targets

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图 2 扩展目标真实航迹(SCR=13 dB)

Figure 2. Real trajectories of the extended targets (SCR=13 dB)

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图 3 GIW-PHD单次航迹估计结果(SCR=13 dB)

Figure 3. Track estimation of the GIW-PHD in a run (SCR=13 dB)

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图 4 AI-GIW-PHD单次航迹估计结果(SCR=13 dB)

Figure 4. Track estimation of the AI-GIW-PHD in a run (SCR=13 dB)

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图 5 GIW-PHD单次扩展状态估计结果(SCR=13 dB)

Figure 5. Extended state estimation of the GIW-PHD in a run (SCR=13 dB)

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图 6 AI-GIW-PHD单次扩展状态估计结果(SCR=13 dB)

Figure 6. Extended state estimation of the AI-GIW-PHD in a run (SCR=13 dB)

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图 7 平均OSPA位置误差(SCR=13 dB)

Figure 7. Averaged OSPA location error (SCR=13 dB)

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图 8 平均势估计结果(SCR=13 dB)

Figure 8. Averaged cardinality estimation (SCR=13 dB)

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图 9 平均OSPA势误差(SCR=13 dB)

Figure 9. Averaged OSPA cardinality error (SCR=13 dB)

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图 10 平均OSPA位置误差(SCR=6 dB)

Figure 10. Averaged OSPA location error (SCR=6 dB)

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图 11 平均势估计结果(SCR=6 dB)

Figure 11. Averaged cardinality estimation (SCR=6 dB)

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图 12 平均OSPA势误差(SCR=6 dB)

Figure 12. Averaged OSPA cardinality error (SCR=6 dB)

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