双红外传感器分布式信息融合的研究
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摘要
随着多传感器技术的快速发展，传感器获取的信息的种类和数量大大增加，多传感器信息融合技术应运而生。针对雷达／红外、红外／紫外、紫外／紫外等多传感器信息融合的应用，世界各国的研究人员经过探索和研究，纷纷提出了一些重要的观点和算法，使多传感器信息融合技术迅速发展起来。
本文主要针对双波段红外传感器的特点进行了信息融合方面的研究。旨在解决双红外传感器信息融合技术的实际应用问题。主要研究内容包括分布式信息融合结构的改进，具体的航迹关联、未关联航迹的处理、航迹融合，目标威胁估计五个方面。
针对双波段红外传感器，本文研究并实现了统计和模糊航迹关联算法，包括：加权法、修正法、序贯法、双门限法、NN、KNN、MKNN、模糊双门限法。经过对算法的研究和仿真实验结果的分析，发现这些算法在目标密集、存在交叉的情况下关联性能会严重下降，导致较多的错漏关联。另外，基于统计的航迹关联算法对跟踪滤波器的类型存在限制，跟踪性能的改变直接影响航迹关联的正确率。为了克服以上问题，本文基于模糊集和序贯检测理论的思想，结合双波段被动式红外传感器的特点，提出了一种针对双波段被动式红外传感器的模糊序贯航迹关联算法，仿真实验证明该算法性能优于其他航迹关联算法，更具有实际适用性。
针对传统的“与／或”未关联航迹输出方法增加漏警虚警概率的问题，本文结合红外传感器特点，基于意见一致性决策理论，提出了对未关联航迹的一种新的处理方法。
航迹融合是多传感器信息融合中的一个重要环节，关系到信息融合结果的精确性。本文针对红外传感器的情况，实现和研究了加权、互协方差和自适应三种航迹融合方法。并进行了仿真实验分析和比较。针对系统的实时性要求，本文提出了自适应航迹融合算法的简化方法，并通过仿真实验证明了算法的有效性。
威胁估计是对目标威胁程度的估计。本文根据红外传感器的特点，提出了一种多属性决策的威胁估计的方法。
With fast development of multi-sensor technology, sensors obtain more kinds and quantities information, multi-sensor information fusion technology plays an important role. Researchers from many countries propose important views and algorithms for applications of radar/infrared, infrared/UV, UV/UV multi-sensor fusion, multi-sensor fusion technology develops rapidly.
This thesis researches information fusion methods according to characters of dual-band infrared sensors. This thesis brings forward an improved distributed information fusion structure for its economy and rational structure. And studies and improves track correlation, track fusion and threaten estimation algorithms.
This thesis studies and realizes traditional track correlation methods: weighting method, WCF, double threshold, NN, KNN, MKNN etc according to infrared sensors characters. Through studying algorithms and analyzing simulation experiments results, this thesis finds that performance of these algorithms decline seriously when targets are dense and across. Also, track correlation algorithms based on statistical theory limit type of tracking filter, and change of tracking performance influence the correct rate of correlation directly. For avoiding these problems, based on fuzzy set and sequential detection theory, combining characters of dual-band infrared sensors, the thesis brings forward a fuzzy sequential track correlation algorithm, and proves that performance of the algorithm is better than others through simulation experiments.
Traditional and/or methods for disposing uncorrelated tracks have influence on leak/false rate. The thesis brings forward a new method based on consensus theory, according to characters of infrared sensors.
Track fusion is an important part of information fusion. It concerns accuracy of the result of information fusion. This thesis achieves and studies weight, WCF and adaptive track fusion algorithms, analyses and compares these algorithms through simulation experiments. In addition, because of time limit, the thesis brings forward a predigesting method of self-adaptive track fusion and proves it' s effective by experiments.
Threaten estimation is to analysis the degree of target threaten. Combining characters of infrared sensors, this thesis brings forward a threaten estimation method based on property decision.
本文主要针对双波段红外传感器的特点进行了信息融合方面的研究。旨在解决双红外传感器信息融合技术的实际应用问题。主要研究内容包括分布式信息融合结构的改进，具体的航迹关联、未关联航迹的处理、航迹融合，目标威胁估计五个方面。
针对双波段红外传感器，本文研究并实现了统计和模糊航迹关联算法，包括：加权法、修正法、序贯法、双门限法、NN、KNN、MKNN、模糊双门限法。经过对算法的研究和仿真实验结果的分析，发现这些算法在目标密集、存在交叉的情况下关联性能会严重下降，导致较多的错漏关联。另外，基于统计的航迹关联算法对跟踪滤波器的类型存在限制，跟踪性能的改变直接影响航迹关联的正确率。为了克服以上问题，本文基于模糊集和序贯检测理论的思想，结合双波段被动式红外传感器的特点，提出了一种针对双波段被动式红外传感器的模糊序贯航迹关联算法，仿真实验证明该算法性能优于其他航迹关联算法，更具有实际适用性。
针对传统的“与／或”未关联航迹输出方法增加漏警虚警概率的问题，本文结合红外传感器特点，基于意见一致性决策理论，提出了对未关联航迹的一种新的处理方法。
航迹融合是多传感器信息融合中的一个重要环节，关系到信息融合结果的精确性。本文针对红外传感器的情况，实现和研究了加权、互协方差和自适应三种航迹融合方法。并进行了仿真实验分析和比较。针对系统的实时性要求，本文提出了自适应航迹融合算法的简化方法，并通过仿真实验证明了算法的有效性。
威胁估计是对目标威胁程度的估计。本文根据红外传感器的特点，提出了一种多属性决策的威胁估计的方法。
With fast development of multi-sensor technology, sensors obtain more kinds and quantities information, multi-sensor information fusion technology plays an important role. Researchers from many countries propose important views and algorithms for applications of radar/infrared, infrared/UV, UV/UV multi-sensor fusion, multi-sensor fusion technology develops rapidly.
This thesis researches information fusion methods according to characters of dual-band infrared sensors. This thesis brings forward an improved distributed information fusion structure for its economy and rational structure. And studies and improves track correlation, track fusion and threaten estimation algorithms.
This thesis studies and realizes traditional track correlation methods: weighting method, WCF, double threshold, NN, KNN, MKNN etc according to infrared sensors characters. Through studying algorithms and analyzing simulation experiments results, this thesis finds that performance of these algorithms decline seriously when targets are dense and across. Also, track correlation algorithms based on statistical theory limit type of tracking filter, and change of tracking performance influence the correct rate of correlation directly. For avoiding these problems, based on fuzzy set and sequential detection theory, combining characters of dual-band infrared sensors, the thesis brings forward a fuzzy sequential track correlation algorithm, and proves that performance of the algorithm is better than others through simulation experiments.
Traditional and/or methods for disposing uncorrelated tracks have influence on leak/false rate. The thesis brings forward a new method based on consensus theory, according to characters of infrared sensors.
Track fusion is an important part of information fusion. It concerns accuracy of the result of information fusion. This thesis achieves and studies weight, WCF and adaptive track fusion algorithms, analyses and compares these algorithms through simulation experiments. In addition, because of time limit, the thesis brings forward a predigesting method of self-adaptive track fusion and proves it' s effective by experiments.
Threaten estimation is to analysis the degree of target threaten. Combining characters of infrared sensors, this thesis brings forward a threaten estimation method based on property decision.
引文
(1) 何友，王国宏等．多传感器信息融合及应用．第1版．北京：电子工业出版，2000年．114-166页
(2) 王忆锋，张海联，李茜．多传感器数据融合技术．红外技术，1997，第19卷，第2期：34-36页
(3) 吴陈．单平台多传感器多目标数据融合系统．造船技术，1999年，第6期(总第232期)：28-33页
(4) James Llinas, David L. Hall. An Introduction to Multi-Sensor Data Fusion. Proceedings of the IEEE, 1997, Voi. 85, Issue. 1: 6-23
(5) Dr. Charles P. Bernardin, Dr. Yun-Kung Jack Lin. Multi-Sensor Targeting and Tracking. Aerospace and Electronics Conference, 1988, Proceedings of the IEEE 1988 National, 1988, Vol. 1: 235-239
(6) X. R. Li. Track Association and Track Fusion with Nondeterministic Target Dynamics. IEEETransaction on Aerospace and Electronic Systems, 2002, Vol. 38, No. 2:659-668
(7) Chee-Yee Chong, Shozo Mori, William H. Barker, Kuo-Chu Chang. Architectures and Algorithms for Track Association and Fusion. IEEE AES Systems Magazine, 2000, page: 5-11
(8) 王祁，聂伟．分布式多传感器数据融合．传感器技术，1997，第16卷，第5期：8-10页
(9) 何友，彭应宁．多目标多传感器模糊双门限航迹相关算法．电子学报，1998，第26卷，第3期：15-20页
(10) 李刚，牟之英．多平台多传感器航迹关联算法研究．航空电子技术，2002，第33卷，第3期：37-40页
(11) F.R. Castella. Theoretical Performance of A Multi-sensor Track-to-Track Correlation Technique. IEE Proc. Radar Sonar Navig, 1995, Vol.142, No. 6:281-286
(12) Yoshio Kosuge, Michiko nayashi, Kousuke Hiwatashi, A Practical New Method for Multi-Sensor Track-to-Track Association, Industrial Electronics, Control and Instrumentation. Proceedings of the IECON' 93, International Conference on 15-19 Nov. 1993, Vol. 3:1758-1763
(13) Bar-shalom. Y. On the Track-to-track Correlation Problem. IEEE Transactions on Automatic Control. AC-26, 1981. page: 571-572
(14) B. La Scala. Effects of Cross-Covariance and Resolution on Track Association. ISIF 2000, WeD1-10-15
(15) Pinsky, Lahav Division. Statistical Approach to Track-to-Track Association Problem. Proceedings of the IEEE 1998 National[C].1998. page:471-474
(16) 陈守熤，赵瑛琪．模糊优选(优化)理论与模型．应用数学，1993，第6卷，第1期：1-6页
(17) 汪培庄．模糊集合论及其应用．上海：上海科学技术出版社，1986：1-35页
(18) 赵焕臣，许树柏，和金生．层次分析法．北京；科学出版社，5-26页
(19) Singer R, Kanyuck. Computer Control of Multiple Site Track Correlation. Automatic, 1971, No. 7:455-454
(20) 施德恒，黄国庆．红外警戒与红外对抗系统述评．应用光学，1997，第1卷，第4期：1-9页
(21) 贾沛璋，齐全跃．多目标航迹相关散发及仿真研究．系统仿真学报，2002，第14卷，第1期：11-15页
(22) 赵振宇等．模糊理论和神经网络的基础与应用．第2版．北京：清华大学出版社，广西科学技术出版社，1995年．1-90页
(23) 于秀兰，空间多传感器遥感图像融合分类技术的研究．工学博士学位论文，哈尔滨工业大学，1999年9月：82-91页
(24) R. K. Saha, K. C. Chang. An Efficient Algorithm for Multisensor Track Fusion. IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No.1: 200-210
(25) K. C. Chang, R. K. Saha, ~. Bar-Shalom. Mark Alford, Performance Evaluation of Multisensor Track-to-Track Fusion. IEEE Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems, page: 627-632
(26) 胡洪波，郭徽东．一种多传感器航迹融合算法的仿真分析．舰船科学技术，2002，第24卷(增刊)
(27) Bar-Shalom Y, Camp L. The Effect of Common Process Noise on the Two-Sensor Fused-Track Covariance. IEEE Trans on Aerospace Electronic Systems, 1986, Vol. 22, No. 6:803-805
(28) Chang K C, Saha R K. Bar-Shalom Y. On Optimal Track-to-Track Fusion. IEEE Trans on Aerospace Electronic Systems, 1997, Vol. 33, No. 4:1271-1276
(29) Saha R K. Effect of Common Process Noise on Two-Sensor Track Fusion. AIAAJournal of Guidence, Control and Dynamics, 1996, Vol.19, No. 4:829-835
(30) Saha R K. Track-to-Track Fusion with Dissimilar Sensors. IEEE Trans on Aerospace Electronic Systems, 1996, Vol. 32, No. 3:1021-1029
(31) 乔向东，王宝树，刘城霞．一种改进的自适应航迹融合算法．西安电子科技大学学报(自然科学版)，2002，第29卷，第6期：748-752页
(32) A. T. Alouani, T. R. Rice, R. E. Helmick. On SensorTrackFusion. Proceedings of the American Control Conference, page: 1042-1046
(33) 曲长文，何友，马强．应用多属性决策的威胁评估方法．系统工程与电子技术，2000，第22卷，第5期：26-29页
(34) 刘健，王献锋，聂成．空袭目标威胁程度评估与排序．系统工程理论与实践，2001，第2卷：142-144页
(35) 胡绍勇，郑应平．用模糊运算确定目标的威胁程度．兵工学报，1999，第20卷，第1期：43-46页
(36) 夏佩伦．基于LMMSE估计的多传感器目标航迹融合．电光与控制，2002，第9卷，第2期：
30-33页
(37) 贺明科，王正明，朱炬波．传感器系统误差与目标航迹的分布式估计，中国科学(E辑)2003，第33卷，第3期：257-262页
(38) Bar-Shalom Y. On the Track-to-Track Correlation Problem. IEEE Trans on Automatic Control, 1981, Voi. 26, No. 2:571-572
(39) Simon Haykin, Neural Networks—A Comprehensive Foundation. TsinghuaUniversity Press, 2001, page: 135-137
(40) 宋小全，孙仲康．多传感器机动目标跟踪．电子学报，1997，第25卷，第9期：98-101页
(41) 刘金贵，朱伟良，李达生．基于方位量测的异类传感器航迹关联决策方法．青岛大学学报，2001，第16卷，第3期：8-11页
(42) John R. Hoffman, Ronald P. S. Mahler. Multi-target Miss Distance and Its Applications. Proceedings of the Fifth International on Information Fusion. 2002. page:149-155
(43) 陈非石，孔祥维，赵坤，刘志远，双波段红外传感器信息融合系统中的模糊序贯航迹关联方法，系统仿真学报，2004
(44) Kong Xiangwei, Chen Feishi, Liuzhiyuan, Zong Shaoxiang, Estimation of Sea Ice Movement Using Locally Parallel Matching Model, KES 2003, LNAI 2773, pp:638-645, 2003
(45) 刘志远，孔祥维，吴新建，陈非石，双波段红外传感器目标跟踪集中式航迹起始方法，全国第十届光电技术与系统学术会议，2003
(2) 王忆锋，张海联，李茜．多传感器数据融合技术．红外技术，1997，第19卷，第2期：34-36页
(3) 吴陈．单平台多传感器多目标数据融合系统．造船技术，1999年，第6期(总第232期)：28-33页
(4) James Llinas, David L. Hall. An Introduction to Multi-Sensor Data Fusion. Proceedings of the IEEE, 1997, Voi. 85, Issue. 1: 6-23
(5) Dr. Charles P. Bernardin, Dr. Yun-Kung Jack Lin. Multi-Sensor Targeting and Tracking. Aerospace and Electronics Conference, 1988, Proceedings of the IEEE 1988 National, 1988, Vol. 1: 235-239
(6) X. R. Li. Track Association and Track Fusion with Nondeterministic Target Dynamics. IEEETransaction on Aerospace and Electronic Systems, 2002, Vol. 38, No. 2:659-668
(7) Chee-Yee Chong, Shozo Mori, William H. Barker, Kuo-Chu Chang. Architectures and Algorithms for Track Association and Fusion. IEEE AES Systems Magazine, 2000, page: 5-11
(8) 王祁，聂伟．分布式多传感器数据融合．传感器技术，1997，第16卷，第5期：8-10页
(9) 何友，彭应宁．多目标多传感器模糊双门限航迹相关算法．电子学报，1998，第26卷，第3期：15-20页
(10) 李刚，牟之英．多平台多传感器航迹关联算法研究．航空电子技术，2002，第33卷，第3期：37-40页
(11) F.R. Castella. Theoretical Performance of A Multi-sensor Track-to-Track Correlation Technique. IEE Proc. Radar Sonar Navig, 1995, Vol.142, No. 6:281-286
(12) Yoshio Kosuge, Michiko nayashi, Kousuke Hiwatashi, A Practical New Method for Multi-Sensor Track-to-Track Association, Industrial Electronics, Control and Instrumentation. Proceedings of the IECON' 93, International Conference on 15-19 Nov. 1993, Vol. 3:1758-1763
(13) Bar-shalom. Y. On the Track-to-track Correlation Problem. IEEE Transactions on Automatic Control. AC-26, 1981. page: 571-572
(14) B. La Scala. Effects of Cross-Covariance and Resolution on Track Association. ISIF 2000, WeD1-10-15
(15) Pinsky, Lahav Division. Statistical Approach to Track-to-Track Association Problem. Proceedings of the IEEE 1998 National[C].1998. page:471-474
(16) 陈守熤，赵瑛琪．模糊优选(优化)理论与模型．应用数学，1993，第6卷，第1期：1-6页
(17) 汪培庄．模糊集合论及其应用．上海：上海科学技术出版社，1986：1-35页
(18) 赵焕臣，许树柏，和金生．层次分析法．北京；科学出版社，5-26页
(19) Singer R, Kanyuck. Computer Control of Multiple Site Track Correlation. Automatic, 1971, No. 7:455-454
(20) 施德恒，黄国庆．红外警戒与红外对抗系统述评．应用光学，1997，第1卷，第4期：1-9页
(21) 贾沛璋，齐全跃．多目标航迹相关散发及仿真研究．系统仿真学报，2002，第14卷，第1期：11-15页
(22) 赵振宇等．模糊理论和神经网络的基础与应用．第2版．北京：清华大学出版社，广西科学技术出版社，1995年．1-90页
(23) 于秀兰，空间多传感器遥感图像融合分类技术的研究．工学博士学位论文，哈尔滨工业大学，1999年9月：82-91页
(24) R. K. Saha, K. C. Chang. An Efficient Algorithm for Multisensor Track Fusion. IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No.1: 200-210
(25) K. C. Chang, R. K. Saha, ~. Bar-Shalom. Mark Alford, Performance Evaluation of Multisensor Track-to-Track Fusion. IEEE Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems, page: 627-632
(26) 胡洪波，郭徽东．一种多传感器航迹融合算法的仿真分析．舰船科学技术，2002，第24卷(增刊)
(27) Bar-Shalom Y, Camp L. The Effect of Common Process Noise on the Two-Sensor Fused-Track Covariance. IEEE Trans on Aerospace Electronic Systems, 1986, Vol. 22, No. 6:803-805
(28) Chang K C, Saha R K. Bar-Shalom Y. On Optimal Track-to-Track Fusion. IEEE Trans on Aerospace Electronic Systems, 1997, Vol. 33, No. 4:1271-1276
(29) Saha R K. Effect of Common Process Noise on Two-Sensor Track Fusion. AIAAJournal of Guidence, Control and Dynamics, 1996, Vol.19, No. 4:829-835
(30) Saha R K. Track-to-Track Fusion with Dissimilar Sensors. IEEE Trans on Aerospace Electronic Systems, 1996, Vol. 32, No. 3:1021-1029
(31) 乔向东，王宝树，刘城霞．一种改进的自适应航迹融合算法．西安电子科技大学学报(自然科学版)，2002，第29卷，第6期：748-752页
(32) A. T. Alouani, T. R. Rice, R. E. Helmick. On SensorTrackFusion. Proceedings of the American Control Conference, page: 1042-1046
(33) 曲长文，何友，马强．应用多属性决策的威胁评估方法．系统工程与电子技术，2000，第22卷，第5期：26-29页
(34) 刘健，王献锋，聂成．空袭目标威胁程度评估与排序．系统工程理论与实践，2001，第2卷：142-144页
(35) 胡绍勇，郑应平．用模糊运算确定目标的威胁程度．兵工学报，1999，第20卷，第1期：43-46页
(36) 夏佩伦．基于LMMSE估计的多传感器目标航迹融合．电光与控制，2002，第9卷，第2期：
30-33页
(37) 贺明科，王正明，朱炬波．传感器系统误差与目标航迹的分布式估计，中国科学(E辑)2003，第33卷，第3期：257-262页
(38) Bar-Shalom Y. On the Track-to-Track Correlation Problem. IEEE Trans on Automatic Control, 1981, Voi. 26, No. 2:571-572
(39) Simon Haykin, Neural Networks—A Comprehensive Foundation. TsinghuaUniversity Press, 2001, page: 135-137
(40) 宋小全，孙仲康．多传感器机动目标跟踪．电子学报，1997，第25卷，第9期：98-101页
(41) 刘金贵，朱伟良，李达生．基于方位量测的异类传感器航迹关联决策方法．青岛大学学报，2001，第16卷，第3期：8-11页
(42) John R. Hoffman, Ronald P. S. Mahler. Multi-target Miss Distance and Its Applications. Proceedings of the Fifth International on Information Fusion. 2002. page:149-155
(43) 陈非石，孔祥维，赵坤，刘志远，双波段红外传感器信息融合系统中的模糊序贯航迹关联方法，系统仿真学报，2004
(44) Kong Xiangwei, Chen Feishi, Liuzhiyuan, Zong Shaoxiang, Estimation of Sea Ice Movement Using Locally Parallel Matching Model, KES 2003, LNAI 2773, pp:638-645, 2003
(45) 刘志远，孔祥维，吴新建，陈非石，双波段红外传感器目标跟踪集中式航迹起始方法，全国第十届光电技术与系统学术会议，2003
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