空基多雷达航迹抗差关联算法

doi:10.7527/S1000-6893.2017.21691

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

空基多雷达航迹抗差关联算法

齐林¹, 刘瑜¹, 任华龙², 何友¹

1. 海军航空大学信息融合研究所, 烟台 264001;
2. 海军青岛雷达声呐修理厂, 青岛 266000

收稿日期:2017-08-24 修回日期:2018-01-25 出版日期:2018-03-15 发布日期:2017-12-04
通讯作者: 齐林,E-mail:3278pirate@163.com E-mail:3278pirate@163.com
基金资助:
国家自然科学基金（61471383，61531020，61471379，61102166）

Air-platform multi-radar anti-bias tracks association algorithm

QI Lin¹, LIU Yu¹, REN Hualong², HE You¹

1. Institute of Information Fusion, Naval Aeronautical University, Yantai 264001, China;
2. Naval Garage of Radar and Sonar, Qingdao 266000, China

Received:2017-08-24 Revised:2018-01-25 Online:2018-03-15 Published:2017-12-04
Supported by:
National Natural Science Foundation of China (61471383, 61531020, 61471379, 61102166)

摘要/Abstract

摘要： 基于高斯随机矢量统计特性，推导出一种适用于探测距离较远、系统误差时变、雷达上报目标不一致等复杂环境的空基多雷达航迹抗差关联算法。分解航迹距离矢量，对消系统误差矢量得出适用于3个及3个以上雷达的航迹抗差关联条件和流程。分别设置了目标密集程度、随机误差和系统误差适应性实验验证算法性能。仿真结果表明所提算法的关联准确性和复杂环境适应性相比现有的基于参照拓扑特征的航迹关联算法（RET算法）和基于距离检测的可信关联算法（confidential算法）有较大幅度的提升。

关键词: 航迹关联, 测量误差, 空基, 多雷达, 高斯随机矢量

Abstract: An air-platform multi-radar anti-bias tracks association algorithm is proposed based on the statistical feature of the Gaussian random vector. The algorithm is adaptive to complicated environment such as far detection range, time-varied measurement biases, and different targets reported by different radars. The anti-bias tracks association conditions for three or more radars and the multi-radar anti-bias tracks association flow are proposed based on the difference between bias vectors. Adaptability experiments are established based on three factors including the targets density, random error and sensor bias to validate the performance of the algorithm. Monte Carlo simulations demonstrate that the proposed algorithm can significantly improve the association accuracy and the adaptability to complicated environment compared with the algorithm based on the reference topology feature (RET algorithm) and the confidential algorithm based on bias detection (confidential algorithm).

Key words: track association, measurement bias, air-platform, multi-radar, Gaussian random vector

中图分类号:

V243.2

齐林, 刘瑜, 任华龙, 何友. 空基多雷达航迹抗差关联算法[J]. 航空学报, 2018, 39(3): 321691-321691.

QI Lin, LIU Yu, REN Hualong, HE You. Air-platform multi-radar anti-bias tracks association algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(3): 321691-321691.

参考文献

[1] BAR-SHALOM Y, FORTMANN T E. Tracking and data association[M]. Orlando:Academic Press INC., 1988:266-272.
[2] 何友, 王国宏, 关欣. 信息融合理论及应用[M]. 北京:电子工业出版社, 2010:267-308. HE Y, WANG G H, GUAN X. Information fusion theory with applications[M]. Beijing:Publishing House of Electronics Industry, 2010:267-308(in Chinese).
[3] LIN X D, BAR-SHALOM Y, KIRUBARAJAN T. Exact multi-sensor dynamic bias estimation with local tracks[J]. IEEE Transactions on Aerospace & Electronic Systems, 2004, 40(2):576-590.
[4] ZHENG Z W, ZHU Y S. New least squares registration algorithm for data fusion[J]. IEEE Transactions on Aerospace & Electronic Systems, 2004, 40(4):1410-1416.
[5] HE Y, ZHANG J W. New track correlation algorithms in a multi-sensor data fusion system[J]. IEEE Transactions on Aerospace & Electronic Systems, 2006, 42(4):1359-1371.
[6] 何友, 宋强, 熊伟. 基于傅里叶变换的航迹对准关联算法[J]. 航空学报, 2010, 31(2):356-362. HE Y, SONG Q, XIONG W. A track registration-correlation algorithm based on Fourier transform[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(2):356-362(in Chinese).
[7] 衣晓, 张怀巍, 曹昕莹, 等. 基于区间灰数的分布式多目标航迹关联算法[J]. 航空学报, 2013, 34(2):352-360. YI X, ZHANG H W, CAO X Y, et al. A track association algorithm for distributed multi-target systems based on gray interval numbers[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(2):352-360(in Chinese).
[8] 石玥, 王钺, 王树刚, 等. 基于目标参照拓扑的模糊航迹关联方法[J]. 国防科技大学学报, 2006, 28(4):105-109. SHI Y, WANG Y, WANG S G, et al. Fuzzy data association based on target topology of reference[J]. Journal of National University of Defence Technology, 2006, 28(4):105-109(in Chinese).
[9] TIAN W, WANG Y, SHAN X M, et al. Track-to-track association for biased data based on the reference topology feature[J]. IEEE Signal Processing Letters, 2014, 21(4):449-453.
[10] 田威. 复杂环境下多传感器航迹关联与抗差处理[D]. 北京:清华大学, 2014:96-116. TIAN W. Multi-sensor track-to-track association and bias removal in complex environments[D]. Beijing:Tsinghua University, 2014:96-116(in Chinese).
[11] ZHU H Y, HAN S Y. Track-to-track association based on structural similarity in the presence of sensor biases[J]. Journal of Applied Mathematics, 2014(1):1-8.
[12] ZHU H Y, WANG W, WANG C. Robust track-to-track association in the presence of sensor biases and missed detections[J]. Information Fusion, 2016, 27:33-40.
[13] LI Z, CHEN S, LEUNG H, et al. Joint data association, registration, and fusion using EM-KF[J]. IEEE Transactions on Aerospace & Electronic Systems, 2010, 46(2):496-507.
[14] ZHU H, LEUNG H, YUEN K. A joint data association, registration, and fusion approach for distributed tracking[J]. Information Sciences, 2015, 324(C):186-196.
[15] MORI S, CHANG K C, CHONG C Y. Tracking performance evaluation-prediction of track purity[C]//Proceeding of SPIE. Bellingham, WA:SPIE, 1989:215-223.
[16] MORI S, CHANG K C, CHONG C Y. Performance prediction of feature-aided track-to-track association[J]. IEEE Transactions on Aerospace & Electronic Systems, 2014, 50(4):2593-2603.
[17] 齐林, 崔亚奇, 熊伟, 等. 基于距离检测的自动识别系统和对海雷达航迹抗差关联算法[J]. 电子与信息学报, 2015, 37(8):1855-1861. QI L, CUI Y Q, XIONG W, et al. Anti-bias association algorithm for automatic identification system and radar based on bias detection[J]. Journal of Electronics & Information Technology, 2015, 37(8):1855-1861(in Chinese).
[18] ZHU H Y, CHEN S. Track fusion in the presence of sensor biases[J]. IET Signal Process, 2014, 8(9):958-967.
[19] ZHU H Y, WANG C. Joint track-to-track association and sensor registration at the track level[J]. Digital Signal Processing, 2015, 41:48-59.
[20] QI L, DONG K, LIU Y, et al. Anti-bias track-to-track association algorithm based on distance detection[J]. IET Radar, Sonar & Navigation, 2017, 11(2):269-276.
[21] 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, 2013:36-50. HE Y, XIU J J, GUAN X. Radar data processing with applications[M]. 3rd ed. Beijing:Publishing House of Electronics Industry, 2013:36-50(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

空基多雷达航迹抗差关联算法

Air-platform multi-radar anti-bias tracks association algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	周庆勇, 魏子卿, 雷耀虎, 刘思伟, 郝晓龙, 吴富梅, 杨彦佶, 强鹏飞. 面向脉冲星深空基准建立的X射线望远镜及发展设想[J]. 航空学报, 2023, 44(3): 526608-526608.
[2]	杨家鑫, 唐圣金, 李良, 孙晓艳, 祁帅, 司小胜. 基于多源信息的隐含非线性维纳退化过程剩余寿命预测[J]. 航空学报, 2023, 44(12): 227662-227662.
[3]	苏子康, 陈海通, 李春涛, 邢卓琳, 王宏伦. 非匹配包线下无人机空基回收拖曳系统协调运动规划[J]. 航空学报, 2023, 44(10): 327377-327377.
[4]	苏子康, 徐忠楠, 李春涛, 陈海通, 王宏伦. 伸缩套臂式无人机空基回收建模与对接控制[J]. 航空学报, 2023, 44(1): 326315-326315.
[5]	衣晓, 杜金鹏, 张天舒. 多局部节点异步抗差航迹关联算法[J]. 航空学报, 2021, 42(6): 324494-324494.
[6]	衣晓, 杜金鹏. 基于分段序列离散度的异步航迹关联算法[J]. 航空学报, 2020, 41(7): 323694-323694.
[7]	张天宇, 郑坚, 田卓尔, 荣英佼, 郭云飞, 申屠晗. 基于PTE的多雷达航迹融合算法[J]. 航空学报, 2019, 40(8): 322848-322848.
[8]	李保珠, 董云龙, 丁昊, 关键. 基于高斯混合模型的航迹抗差关联算法[J]. 航空学报, 2019, 40(6): 322650-322650.
[9]	关欣, 彭彬彬, 衣晓. 修正极坐标系下雷达与ESM航迹对准关联[J]. 航空学报, 2017, 38(5): 320668-320668.
[10]	衣晓, 韩健越, 张怀巍, 关欣. 基于区实混合序列相似度的异步不等速率航迹关联算法[J]. 航空学报, 2015, 36(4): 1212-1220.
[11]	田威, 王钺, 山秀明, 杨健. 基于一致关联数最大化的航迹关联算法[J]. 航空学报, 2014, 35(11): 3115-3122.
[12]	衣晓, 张怀巍, 曹昕莹, 何友. 基于区间灰数的分布式多目标航迹关联算法[J]. 航空学报, 2013, 34(2): 352-360.
[13]	刘伟峰, 文成林. 基于OSPA距离的航迹关联方法[J]. 航空学报, 2012, (6): 1083-1092.
[14]	叶钒, 何峰, 朱炬波, 张永胜. 基于稀疏表示的多雷达信号二维融合处理[J]. 航空学报, 2011, 32(3): 515-521.
[15]	苗赢;孙兆伟. 星载GPS测量数据预处理方法研究[J]. 航空学报, 2010, 31(3): 602-607.