回波幅度信息辅助的群目标航迹起始方法

靳标; 李聪; 张贞凯

doi:10.12000/JR19088

回波幅度信息辅助的群目标航迹起始方法

DOI: 10.12000/JR19088

靳标^1, ,,
李聪^2,,
张贞凯^1,

1.
江苏科技大学电子信息学院镇江 212003
2.
西安交通大学电子与信息工程学院西安 710049

基金项目: 国家自然科学基金(61701416, 61871203)，中国博士后科学基金(2017M613214)

详细信息

作者简介:
靳　标(1986–)，男，山西翼城人，博士，江苏科技大学电信学院副教授，研究方向为雷达信号检测与目标跟踪，认知雷达发射波形设计等。E-mail: biaojin@just.edu.cn

李　聪(1994–)，男，河北唐山人。现为西安交通大学电子与信息学部硕士研究生。主要研究方向为阵列信号处理，智能天线系统。E-mail: a1870692245@163.com

张贞凯(1982–)，男，江苏徐州人，博士，副教授，2013年在南京航空航天大学电子信息工程学院获得博士学位，现担任江苏科技大学电子信息学院副教授，主要研究方向为雷达通信一体化技术、雷达信号处理、水下目标定位等研究，目前已发表论文78篇。E-mail: zhangzhenkai@just.edu.cn

通讯作者:
靳标 biaojin@just.edu.cn

责任主编：王海鹏 Corresponding Editor: WANG Haipeng
中图分类号: TN957.52
计量
- 文章访问数: 3079
- HTML全文浏览量: 1104
- PDF下载量: 221
- 被引次数: 29
出版历程
- 收稿日期: 2019-09-27
- 修回日期: 2019-12-17
- 网络出版日期: 2020-08-28

Group Target Track Initiation Method Aided by Echo Amplitude Information

JIN Biao^{1
, ,},
LI Cong^2
,,
ZHANG Zhenkai^1
,

1.
College of Electronics Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2.
The School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Funds: The National Natural Science Foundation of China (61701416, 61871203), The Project funded by China Postdoctoral Science Foundation (2017M613214)

More Information

Corresponding author: JIN Biao, biaojin@just.edu.cn

摘要

摘要: 航迹起始是群目标跟踪的首要环节，其性能好坏直接影响着目标跟踪航迹的质量。传统的群目标航迹起始方法仅利用目标的位置信息完成分群检测和等效量测求解等步骤，没有充分利用回波幅度信息，存在分群检测不理想、等效量测求解不准确等问题，有可能引起失跟现象。针对此问题，该文提出一种回波幅度信息辅助的群目标航迹起始方法。首先利用目标位置信息和幅度信息完成分群检测，然后综合采用幅度加权和位置加权求解等效量测，最后基于修正的逻辑法进行群目标航迹起始。该文方法在分群检测和求解等效量测等步骤充分利用了回波幅度信息，不仅可以在集群数量未知的情况下正确划分群，而且降低了失跟率，提高了群目标的跟踪性能。仿真结果验证了所提方法的有效性。
- 目标跟踪 /
- 群目标 /
- 航迹起始 /
- 幅度信息 /
- 聚类
Abstract: Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results.
- Target tracking /
- Group target /
- Track initiation /
- Amplitude information /
- Cluster

HTML全文

图 1 昆虫种群空间密度分布场景

Figure 1. Spatial density distribution of insect population

下载: 全尺寸图片幻灯片

图 2 本文算法的流程

Figure 2. The flow of the algorithm in this paper

下载: 全尺寸图片幻灯片

图 3 分群检测的具体流程

Figure 3. The specific process of cluster detection

下载: 全尺寸图片幻灯片

图 4 量测点迹空间分布

Figure 4. The spatial distribution of measuring point trace

下载: 全尺寸图片幻灯片

图 5 基于位置信息的分群结果

Figure 5. The clustering result based on location information

下载: 全尺寸图片幻灯片

图 6 幅度信息辅助的分群结果

Figure 6. The clustering result aided by echo amplitude information

下载: 全尺寸图片幻灯片

图 7 等效量测对比

Figure 7. Equivalent measurement comparison

下载: 全尺寸图片幻灯片

图 8 不同权值大小时的起始航迹对比

Figure 8. Comparison of initial tracks with different weights

下载: 全尺寸图片幻灯片

表 1 幅度、距离不同权值下航迹起始成功率

Table 1. The success rate of track start under different weights of amplitude and distance

	$\alpha = 0.8, \beta = 0.2$	$\alpha = 0.5, \beta = 0.5$	$\alpha = 0.2, \beta = 0.8$	$\alpha = 0.1, \beta = 0.9$	$\alpha = 0, {\rm{}}\beta = 1.0$
航迹起始成功率(%)	80	93	98	95	89

下载: 导出CSV

参考文献(20)

[1]	NIEDFELDT P C and BEARD R W. Convergence and complexity analysis of recursive-RANSAC: A new multiple target tracking algorithm[J]. IEEE Transactions on Automatic Control, 2016, 61(2): 456–461.
[2]	ÚBEDA-MEDINA L, GARCÍA-FERNÁNDEZ Á F, and GRAJAL J. Adaptive auxiliary particle filter for track-before-detect with multiple targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(5): 2317–2330. doi: 10.1109/TAES.2017.2691958
[3]	ZHU Yun, WANG Jun, and LIANG Shuang. Efficient joint probabilistic data association filter based on Kullback-Leibler divergence for multi-target tracking[J]. IET Radar, Sonar & Navigation, 2017, 11(10): 1540–1548.
[4]	VIVONE G and BRACA P. Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 1007–1019. doi: 10.1109/JOE.2015.2503499
[5]	LIAN Feng, HAN Chongzhao, LIU Weifeng, et al. Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets[J]. Signal Processing, 2012, 92(7): 1729–1744. doi: 10.1016/j.sigpro.2012.01.009
[6]	CAO Xianbin, JIANG Xiaolong, LI Xiaomei, et al. Correlation-based tracking of multiple targets with hierarchical layered structure[J]. IEEE Transactions on Cybernetics, 2018, 48(1): 90–102. doi: 10.1109/TCYB.2016.2625320
[7]	王聪, 王海鹏, 何友, 等. 基于ICP的稳态部分可辨编队目标精细跟踪算法[J]. 北京航空航天大学学报, 2017, 43(6): 1123–1131. WANG Cong, WANG Haipeng, HE You, et al. Refined tracking algorithm for steady partly resolvable group targets based on ICP[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(6): 1123–1131.
[8]	方琳琳, 周超, 王锐, 等. 昆虫目标雷达散射截面积特征辅助跟踪算法[J]. 雷达学报, 2019, 8(5): 598–605. doi: 10.12000/JR19067 FANG Linlin, ZHOU Chao, WANG Rui, et al. RCS feature-aided insect target tracking algorithm[J]. Journal of Radars, 2019, 8(5): 598–605. doi: 10.12000/JR19067
[9]	封洪强. 雷达在昆虫学研究中的应用[J]. 植物保护, 2011, 37(5): 1–13. doi: 10.3969/j.issn.0529-1542.2011.05.001 FENG Hongqiang. Applications of radar in entomological research[J]. Plant Protection, 2011, 37(5): 1–13. doi: 10.3969/j.issn.0529-1542.2011.05.001
[10]	耿文东. 基于群目标几何中心的群起始算法研究[J]. 系统工程与电子技术, 2008, 30(2): 269–272. doi: 10.3321/j.issn:1001-506X.2008.02.019 GENG Wendong. Study of group-initialization method based on group-target center of geometry[J]. Systems Engineering and Electronics, 2008, 30(2): 269–272. doi: 10.3321/j.issn:1001-506X.2008.02.019
[11]	周大庆, 耿文东, 倪春雷. 基于编队目标重心的航迹起始方法研究[J]. 无线电工程, 2010, 40(2): 32–34. doi: 10.3969/j.issn.1003-3106.2010.02.011 ZHOU Daqing, GENG Wendong, and NI Chunlei. Study of track initiation method based on barycenter of formation target[J]. Radio Engineering of China, 2010, 40(2): 32–34. doi: 10.3969/j.issn.1003-3106.2010.02.011
[12]	KOCH J W. Bayesian approach to extended object and cluster tracking using random matrices[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042–1059. doi: 10.1109/TAES.2008.4655362
[13]	韩玉兰, 朱洪艳, 韩崇昭. 采用随机矩阵的多扩展目标滤波器[J]. 西安交通大学学报, 2015, 49(7): 98–104. doi: 10.7652/xjtuxb201507017 HAN Yulan, ZHU Hongyan, and HAN Chongzhao. A multi-target filter based on random matrix[J]. Journal of Xi’an Jiaotong University, 2015, 49(7): 98–104. doi: 10.7652/xjtuxb201507017
[14]	JIN Biao, JIU Bo, SU Tao, et al. Switched Kalman filter-interacting multiple model algorithm based on optimal autoregressive model for manoeuvring target tracking[J]. IET Radar, Sonar & Navigation, 2015, 9(2): 199–209.
[15]	GNING A, MIHAYLOVA L, MASKELL S, et al. Ground target group structure and state estimation with particle filtering[C]. The 2008 11th International Conference on Information Fusion, Cologne, Germany, 2008: 1–8.
[16]	LI Yunxiang, XIAO Huaitie, SONG Zhiyong, et al. A new multiple extended target tracking algorithm using PHD filter[J]. Signal Processing, 2013, 93(12): 3578–3588. doi: 10.1016/j.sigpro.2013.05.011
[17]	LAN Jian and LI X R. Extended-object or group-target tracking using random matrix with nonlinear measurements[J]. IEEE Transactions on Signal Processing, 2019, 67(19): 5130–5142. doi: 10.1109/TSP.2019.2935866
[18]	MIHAYLOVA L, CARMI A Y, SEPTIER F, et al. Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking[J]. Digital Signal Processing, 2014, 25: 1–16. doi: 10.1016/j.dsp.2013.11.006
[19]	DANIYAN A, LAMBOTHARAN S, DELIGIANNIS A, et al. Bayesian multiple extended target tracking using labeled random finite sets and splines[J]. IEEE Transactions on Signal Processing, 2018, 66(22): 6076–6091. doi: 10.1109/TSP.2018.2873537
[20]	何友, 修建娟, 关欣. 雷达数据处理及应用[M]. 3版. 北京: 电子工业出版社, 2013: 108–174. HE You, XIU Jianjuan, and GUAN Xin. Radar Data Processing with Applications[M]. 3rd ed. Beijing: Publishing House of Electronics Industry, 2013: 108–174.

施引文献

期刊类型引用(13)

1.	赵金奇，李宇轩，刘子蓉，安庆，宋时雨，牛玉芬. 基于相似性衡量函数优化的SAR时空极化信息一体化洪涝变化检测方法. 测绘学报. 2024(12): 2375-2390 . 百度学术
2.	庄会富，王鹏，苏亚男，张祥，范洪冬. 基于多源时序SAR数据的涿州洪涝淹没动态监测. 自然资源遥感. 2024(04): 218-228 . 百度学术
3.	赵维谚，沈志，徐真，杨亮，雷明阳. 基于增强学习机制的SAR图像水域分割方法. 计算机应用与软件. 2023(05): 262-265+337 . 百度学术
4.	王磊，连增增. 基于Sentinel-1A的2020年鄱阳湖流域洪水灾害遥感监测. 地理空间信息. 2022(06): 43-46 . 百度学术
5.	李宁，郭志顺，毋琳，赵建辉. River-Net：面向河道提取的Refined-Lee Kernel深度神经网络模型. 雷达学报. 2022(03): 324-334 . 本站查看
6.	黄平平，段盈宏，谭维贤，徐伟. 基于融合差异图的变化检测方法及其在洪灾中的应用. 雷达学报. 2021(01): 143-158 . 本站查看
7.	董天成，杨肖，李卉，张志，齐睿. 基于Faster R-CNN和MorphACWE模型的SAR图像高原湖泊提取. 国土资源遥感. 2021(01): 129-137 . 百度学术
8.	李宁，吕宗森，郭拯危. 联合变化检测与子带对消技术的SAR图像干扰抑制方法. 系统工程与电子技术. 2021(09): 2484-2492 . 百度学术
9.	郭山川，杜培军，蒙亚平，王欣，唐鹏飞，林聪，夏俊士. 时序Sentinel-1A数据支持的长江中下游汛情动态监测. 遥感学报. 2021(10): 2127-2141 . 百度学术
10.	李宁，牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法. 雷达学报. 2020(01): 174-184 . 本站查看
11.	吴瑞娟，何秀凤，王静. 结合像元级与对象级的滨海湿地变化检测方法. 地球信息科学学报. 2020(10): 2078-2087 . 百度学术
12.	冀广宇，董勇伟，卜运成，李焱磊，周良将，梁兴东. 基于目标相干性表征差异的多波段SAR相干变化检测方法. 雷达学报. 2018(04): 455-464 . 本站查看
13.	牛世林，郭拯危，李宁，毋琳，赵建辉. 星载SAR水域分割研究进展与趋势分析. 聊城大学学报(自然科学版). 2018(02): 72-86 . 百度学术