SPA和DS证据理论的多传感器航迹关联方法

doi:10.3969/j.issn.1009-086x.2018.06.012

现代防御技术 ›› 2018, Vol. 46 ›› Issue (6): 75-79.DOI: 10.3969/j.issn.1009-086x.2018.06.012

SPA和DS证据理论的多传感器航迹关联方法

杨军佳, 叶晨亮, 刘志青, 廉博

陆军炮兵防空兵学院郑州校区, 河南郑州 450000

出版日期:2018-11-30 发布日期:2020-10-22
作者简介:杨军佳(1987-), 男, 河南商水人。讲师, 硕士, 研究方向为雷达组网及作战使用。通信地址:450000 河南省郑州市二七区建设东路24号侦察预警教研室 E-mail:yangjunjia.love@163.com

Multi-Sensor Track Association Method Based on SPA and DS Evidence Theory

YANG Jun-jia, YE Chen-liang, LIU Zhi-qing, LIAN Bo

Army Academy of Artilery and Air Defense, Zhengzhou Campus, Henan Zhengzhou 450000, China

Online:2018-11-30 Published:2020-10-22

摘要/Abstract

摘要： 针对多传感器航迹关联问题, 提出了一种基于集对分析和DS证据理论的解决方法。利用集对分析理论将不同传感器之间航迹信息按照集对处理, 建立航迹间的联系度表达式, 在集对贴近度的基础之上, 构建集对贴近度矩阵。利用DS证据理论来解决航迹关联问题。实例表明, 能够较好地解决多传感器航迹关联问题。

关键词: 多传感器, 航迹关联, 集对联系数, 集对贴近度, 贴近度矩阵, DS证据理论

Abstract: A method of multi-sensor track association based on set pair analysis (SPA) and Dempster Shafer (DS) evidence theory is presented. In consideration of systematic error, the track information between different sensors is handled in the form of set pair based on the SPA closeness, and the expression of connection degree between tracks is built. On the basis of the closeness degree of set pair, the matrix of closeness degree is constructed. The problem of track association is solved by DS evidence theory. The example shows that this method can solve the question of multi sensor track association preferably.

Key words: multi-sensor, track association, set pair connection number, set pair closeness, closeness matrix, Dempster Shafer (DS) evidence theory

中图分类号:

杨军佳, 叶晨亮, 刘志青, 廉博. SPA和DS证据理论的多传感器航迹关联方法[J]. 现代防御技术, 2018, 46(6): 75-79.

YANG Jun-jia, YE Chen-liang, LIU Zhi-qing, LIAN Bo. Multi-Sensor Track Association Method Based on SPA and DS Evidence Theory[J]. Modern Defense Technology, 2018, 46(6): 75-79.

参考文献 15

[1]	石教华, 张靖.数理统计方法在航迹关联中的应用[J].中国电子科学研究院学报, 2013, 8(3):255-258.SHI Jiao-hua, ZHANG Jing.Application of Mathematical Statistics Method for Track Correlation[J].Journal of CAEIT, 2013, 8(3):255-258.
[2]	韩国伟, 张冰, 朱志宇, 等.基于综合相似接近度的航迹关联算法[J].弹箭与制导学报, 2009, 29(2):279-281HAN Guo-wei, ZHANG Bing, ZHU Zhi-yu, et al.Track-Association Algorithm Based on Synthetical Similar Approach Degree[J].Journal of Projectiles, Rockets, Missiles and Guidance, 2009, 29(2):279-281.
[3]	张翔宇, 王国宏, 王娜, 等.系统误差下异地配置的雷达和电子支援测量航迹关联[J].电光与控制, 2012, 19(3):30-36.ZHANG Xiang-yu, WANG Guo-hong, WANG Na, et al.Track Association of Radar and ESM Sensors with Systematic Errors at Different Sites[J].Electronics Optics ＆ Control, 2012, 19(3):30-36.
[4]	杨博帆, 李延磊, 岳韶华.基于直觉模糊的双门限航迹关联算法[J].空军工程大学学报:自然科学版, 2014, 15(5):30-33.YANG Bo-fan, LI Yan-lei, YUE Shao-hua.A Track Association Algorithm on Intutionistic Fuzzy Bi-Threshold[J].Journal of Air Force Engineerign University:Natural Science ed, 2014, 15(5):30-33.
[5]	刘德浩, 王国宏, 陈中华.系统误差下的模糊航迹关联方法[J].现代防御技术, 2012, 40(3):128-132.LIU De-hao, WANG Guo-hong, CHEN Zhong-hua.Fuzzy Track Alignment-Correlation Algorithm with Systematic Error[J]. Modern Defence Technology, 2012, 40(3):128-132.
[6]	熊伟, 高峰, 王海鹏.系统误差下航迹关联算法[J].火力与指挥控制, 2012, 37(11):29-33.XIONG Wei, GAO Feng, WANG Hai-peng. Research of Track Association with Systemstic Errors[J]. Fire Control ＆ Command Control, 2012, 37(11):29-33.
[7]	张良, 陶海军, 杨钒, 等.模糊聚类的多雷达航迹关联算法[J].现代防御技术, 2017, 45(6):113-117.ZHANG Liang, TAO Hai-jun, YANG Fan, et al.Multiradar Track Association Algorithm Based on Fuzzy Clustering[J].Modern Defence Technology, 2017, 45(6):113-117.
[8]	李首庆, 徐洋.模糊聚类法改进异类传感器航迹关联[J].现代防御技术, 2017, 45(2):124-130.LI Shou-qing, XU Yang.Using Fuzzy Clustering Algorithm to Improved Heterogeneous Sensors Track Correlation[J]. Modern Defence Technology, 2017, 45(2):124-130.
[9]	张树斌, 方洋旺, 雍霄驹, 等.分步的基于模糊聚类的多雷达航迹关联算法[J].计算机应用, 2016, 36(5):1232-1235.ZHANG Shu-bin, FANG Yang-wang, YONG Xiao-ju, et al.Step-by-Step Multi-Radar Track Correlation Algorithm Based on Fuzzy Clustering[J].Journal of Computer Applications, 2016, 36(5):1232-1235.
[10]	刘根旺, 刘永信, 纪永刚, 等.基于模糊双门限的高频地波雷达与AIS目标航迹关联方法[J].系统工程与电子技术, 2016, 38(3):557-562.LIU Gen-wang, LIU Yong-xin, JI Yong-gang, et al.Track Association for High-Frequency Surface Wave Radar and AIS Based on Fuzzy Double Threshold Theory[J].Systems Engineering and Electronics, 2016, 38(3):557-562.
[11]	朱璟, 彭展.灰色航迹关联算法的分析与改进[J].现代雷达, 2015, 37(8):19-24.ZHU Jing, PENG Zhan.Analysis and Improvement of the Grey Track-Track Association Algorithm[J].Modern Radar, 2015, 37(8):19-24.
[12]	尉强, 刘忠.一种粗-精结合的航天侦察航迹关联算法[J].雷达科学与技术, 2017, 15(1):29-34.WEI Qiang, LIU Zhong.A Track Correlation Algorithm of Space-Based Reconnaissance Based on Rough and Precise Correlations[J].Radar Science and Technology, 2017, 15(1):29-34.
[13]	高峰, 谢小平, 熊伟.基于广义绝对灰关联度的航迹关联算法[J].雷达科学与技术, 2016, 14(6):642-647.GAO Feng, XIE Xiao-ping, XIONG Wei.Track Correlation Algorithm Based on Generalized Absolute Grey Association Degree[J]. Radar Science and Technology, 2016, 14(6):642-647.
[14]	ZHAO Ke-qin.Based on Set Pair Analysis Confrontation Classification, Measurement and Application[J].Science, Technology and Dialectics, 1994(2):26-30.
[15]	ZHAO Ke-qin.Based on Set Pair Analysis of the Size Relation Characterization and Application[J].Non-Ferrous Metals Engineering & Research, 1996, 17(3):59-62.

SPA和DS证据理论的多传感器航迹关联方法

Multi-Sensor Track Association Method Based on SPA and DS Evidence Theory

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 15

相关文章 8

编辑推荐

Metrics

本文评价

[1]	薄钧天, 王国宏, 于洪波, 张敬艳. 大时空差条件下高超声速目标检测关联算法[J]. 现代防御技术, 2021, 49(5): 56-64.
[2]	谭顺成, 陈中华, 于洪波. 分布式2D雷达网的航迹关联方法[J]. 现代防御技术, 2021, 49(4): 49-55.
[3]	俞宙, 单甘霖, 段修生. 面向区域覆盖的多传感器优化布站[J]. 现代防御技术, 2018, 46(6): 94-101.
[4]	刘标, 许腾, 刘树锋. 水下目标识别的规划融合算法[J]. 现代防御技术, 2018, 46(6): 64-67.
[5]	董晨, 刘兴科, 周金鹏, 陆志沣. 导弹防御多传感器协同探测任务规划[J]. 现代防御技术, 2018, 46(6): 57-63.
[6]	林菡, 李昌玺, 陈丽娟. MIMO-FNN模型的弹道导弹目标识别方法[J]. 现代防御技术, 2018, 46(6): 36-43.
[7]	熊娜, 张孝东, 李飞, 马超. 复杂系统工程作业贝叶斯网络风险方法研究[J]. 现代防御技术, 2018, 46(5): 114-121.
[8]	刘标, 许腾, 李光. D-S证据理论改进算法提高水下目标识别准确性[J]. 现代防御技术, 2018, 46(1): 120-124.