昆虫目标雷达散射截面积特征辅助跟踪算法

方琳琳; 周超; 王锐; 胡程

doi:10.12000/JR19067

昆虫目标雷达散射截面积特征辅助跟踪算法

DOI: 10.12000/JR19067

方琳琳^{1, 2,},
周超^{1, 2, ,},
王锐^{1, 2},
胡程^{1, 2}

1.
北京理工大学信息与电子学院雷达技术研究所北京 100081
2.
卫星导航电子信息技术教育部重点实验室(北京理工大学) 北京 100081

基金项目: 国家自然科学基金(31727901)

详细信息

作者简介:
方琳琳(1993–)，女，北京理工大学雷达技术研究所博士生，研究方向为雷达目标检测跟踪算法。E-mail: fanglinlin_1@163.com

周　超(1987–)，男，博士后，工作于北京理工大学雷达技术研究所，研究方向为雷达抗干扰、雷达目标检测跟踪算法。E-mail: 7520180050@bit.edu.cn

胡　程(1981–)，男，湖南岳阳人，博士，教授。2009年在北京理工大学信息与电子学院获得博士学位，现担任北京理工大学信息与电子学院教授，博士生导师，主要研究方向为地球同步轨道SAR、双基地/前向散射雷达信号处理和昆虫雷达等。E-mail: hucheng.bit@gmail.com

通讯作者:
周超　7520180050@bit.edu.cn

责任主编：王国宏 Corresponding Editor: WANG Guohong
中图分类号: TN959.4
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出版历程
- 收稿日期: 2019-07-12
- 修回日期: 2019-09-23
- 网络出版日期: 2019-10-01

RCS Feature-aided Insect Target Tracking Algorithm

FANG Linlin^{1, 2
,},
ZHOU Chao^{1, 2
, ,},
WANG Rui^{1, 2},
HU Cheng^{1, 2}

1.
Radar Research Lab, School of Information and Electronics, Institute of Technology, Beijing 100081, China
2.
Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute ofTechnology), Ministry of Education, Beijing 100081, China

Funds: The National Natural Science Foundation of China (31727901)

More Information

Corresponding author: ZHOU Chao, 7520180050@bit.edu.cn

摘要

摘要: 害虫迁飞具有规模大、突发性强的特点，会导致病虫害异地大爆发，粮食产量下降，造成重大的经济损失。昆虫雷达是监测迁飞性害虫的一种有效手段。昆虫目标的雷达散射截面积(RCS)较小，回波能量弱，在保证高检测率的同时会带来高虚警率问题，进而导致在目标跟踪的数据关联环节，易受虚假量测的影响出现关联错误。幅度特征辅助跟踪算法利用目标与噪声点迹的幅度差异，可以有效提高目标与噪声的识别度，改善跟踪性能，但是其需要已知目标的RCS起伏模型作为先验信息来计算幅度似然比。因此，该文基于Ku波段高分辨昆虫雷达外场实测昆虫回波数据，分析了昆虫目标的RCS起伏特性，得出Gamma分布可以较好地拟合昆虫目标的RCS统计分布，并将其作为先验信息，推导出Gamma起伏目标在高斯白噪声背景下的幅度似然比。通过在不同信噪比、不同量测噪声及不同起伏模型参数下的仿真结果及性能指标分析，验证了相比于概率数据互联滤波算法(PDAF)算法，目标RCS特征辅助的跟踪算法可以有效提高昆虫目标的跟踪精度。
- 昆虫雷达 /
- 目标跟踪 /
- 雷达截面积起伏 /
- 特征辅助
Abstract: Pest migration has the characteristics of large scale and strong suddenness, which will lead to the outbreaks of pests and diseases, the decline of grain yield, and considerable economic losses. Entomological radar is an effective means of monitoring migratory pests. However, the Radar Cross Section (RCS) of an insect target is small, whereas the echo power is weak. High detection probability will result in a high false alarm probability. In the data association step of target tracking, the association error occurs due to the influence of false measurement. By utilizing the amplitude difference between the target and noise, the amplitude information-assisted tracking algorithm can effectively improve the recognition degree toward the target and noise and improve the tracking performance. However, the RCS fluctuation model of the target is needed as prior information to calculate the amplitude likelihood ratio. Therefore, in this paper, the insect RCS fluctuating characteristics are analyzed based on Ku-band entomological radar experiment data. The results show that gamma distribution can fit well the RCS probability distribution of the insect target. On this basis, we derive the amplitude likelihood ratio of the gamma fluctuation target in Gaussian white-noise background. By analyzing the simulation results and performance under different signal-to-noise ratios, measurement noises, and fluctuation model parameters, compared with probabilistic data association filter, the RCS feature-aided tracking algorithm can effectively improve the insect target tracking accuracy.
- Entomological radar /
- Target tracking /
- Radar Cross Section fluctuating /
- Feature aided

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图 1 昆虫目标RCS起伏建模流程图

Figure 1. The flow chart of insect RCS fluctuation modeling

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图 2 实验场景

Figure 2. The experimental scene

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图 3 PDF拟合结果

Figure 3. PDF fitting results

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图 4 拟合误差

Figure 4. Fitting error

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图 5 CDF拟合结果

Figure 5. CDF fitting results

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图 6 S检验参数

Figure 6. K-S test parameters

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图 7 目标RCS辅助跟踪算法实现框图

Figure 7. RCS aided tracking algorithm implementationblock diagram

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图 8 仿真场景示意图

Figure 8. Simulation scene

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图 9 不同SNR下跟踪误差比较

Figure 9. Comparison of tracking errors under different SNRs

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图 10 有效航迹概率

Figure 10. The probability of effective track

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表 1 仿真参数

Table 1. Simulation parameters

参数	数值	参数	数值
目标RCS起伏模型	伽马分布	杂波点幅度模型	瑞利分布
起伏模型参数1	Ω=1.8, v=2.0	起伏模型参数2	Ω=2.0, v=3.2
杂波密度$\lambda $	0.00004	SNR	9 dB, 13 dB
虚警概率	10^–4	扫描间隔T	1 s
跟踪步数	100	波门尺寸	16
过程噪声方差$\sigma _p^2$	0.01 m²	量测噪声标准差$\sigma _R^{}$	1 m
蒙特卡洛仿真次数	500	航迹丢失判别$m$	7帧
第1门限${T_1}$	1 m	第2门限${T_2}$	5 m

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表 2 不同量测噪声下均方根误差均值改善值

Table 2. The improvement of the root mean square error under different measurement noises

量测噪声标准差(m)	SNR(dB)	均方根误差均值改善(m)
1	9	0.14
1	13	0.11
3	9	0.53
3	13	0.42
5	9	1.17
5	13	0.96

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表 3 不同起伏参数下有效航迹改善率

Table 3. The improvement probability of effective track underdifferent fluctuation parameters

SNR(dB)	参数1(%)	参数2(%)
9	7.1	8.3
10	6.7	7.4
11	5.9	6.5
12	4.3	5.0
13	2.5	3.1
14	1.9	2.1
15	1.6	1.8

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参考文献(13)

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