基于多传感器信息融合的车载多目标跟踪算法研究
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摘要
本文分析、总结了数据关联的诸多方法,从应用角度讨论了信息融合的功能模型和融合方法,以及目标跟踪技术中的一些基本要素。论文以科研项目“装甲车辆多传感器信息实时处理系统”作为研究背景,阐明了多目标跟踪所采用的主要算法,并着重分析了在几个关键问题上所采用的处理方案。
首先,文中介绍了数据关联算法中的最近邻算法和数据关联快速算法,并进行了仿真实验和结果对比分析,验证了二者的优缺点。在前人研究的顺序多传感器联合概率数据关联算法基础之上,基于数据关联快速算法,文中提出了一种新的关联算法—顺序数据关联快速算法,该算法的基本原理是：凡是落入跟踪门内的点迹,都认为是所跟踪目标的一个有效回波,只是每个有效回波的互联概率不同,而互联概率是根据有效回波的正态分布计算得到,最后利用第一次滤波结果和观测数据进行第二次滤波以减少误差。同时对该算法和数据关联快速算法进行了仿真及误差对比分析,验证了顺序数据关联快速算法的有效性。
其次,针对战场目标跟踪中雷达获得的目标角度误差大的问题,引入了高精度的红外方位探测信息进行关联,用以修正融合航迹点的方位角,该算法充分利用了红外与雷达的互补性,经仿真实验,降低了目标航迹的方位角和俯仰角跟踪误差,取得了较好的实验效果。
In this paper, many methods of data association are analyzed and summarized. Functional model and fusion methods are discussed from applied angle. In addition, basic elements in targets tracking technology are introduced. Furthermore, based on the project which systemic study for information processed real-time of armored vehicles is introduced, the general framework and working flow are explained and some methods of several key steps have a detailed analysis in this paper.
Firstly, the nearest neighbor for data association (NNDA) and fast algorithm for data association (FAFDA) are introduced in this paper. At the same time, emulation experiment and analysis of emulation result are conducted, which validates merits and defects between NNDA and FAFDA. This paper presents a novel method of data association based on fast algorithm for data association and multiple sensors joint probability data association, which is called multiple sensors fast algorithm for data association (MSFAFDA). The basic theory of MSFAFDA is that all the data in gate are regarded as valid data. But associated probabilities of the valid data are different. The second data are calculated according to the first data and observational data. At the same time, emulation result and error contrast and analysis between NNDA and MSFAFDA validate it.
Secondly, in allusion to question which the precision of targets gained by radar is low, azimuth gained by infrared sensor with high precision is introduced to improve azimuth precision of targets fused. This algorithm makes good use of merits between radar and infrared sensor. Emulation experiment validates that azimuth error of targets fused is reduced rapidly.
首先,文中介绍了数据关联算法中的最近邻算法和数据关联快速算法,并进行了仿真实验和结果对比分析,验证了二者的优缺点。在前人研究的顺序多传感器联合概率数据关联算法基础之上,基于数据关联快速算法,文中提出了一种新的关联算法—顺序数据关联快速算法,该算法的基本原理是：凡是落入跟踪门内的点迹,都认为是所跟踪目标的一个有效回波,只是每个有效回波的互联概率不同,而互联概率是根据有效回波的正态分布计算得到,最后利用第一次滤波结果和观测数据进行第二次滤波以减少误差。同时对该算法和数据关联快速算法进行了仿真及误差对比分析,验证了顺序数据关联快速算法的有效性。
其次,针对战场目标跟踪中雷达获得的目标角度误差大的问题,引入了高精度的红外方位探测信息进行关联,用以修正融合航迹点的方位角,该算法充分利用了红外与雷达的互补性,经仿真实验,降低了目标航迹的方位角和俯仰角跟踪误差,取得了较好的实验效果。
In this paper, many methods of data association are analyzed and summarized. Functional model and fusion methods are discussed from applied angle. In addition, basic elements in targets tracking technology are introduced. Furthermore, based on the project which systemic study for information processed real-time of armored vehicles is introduced, the general framework and working flow are explained and some methods of several key steps have a detailed analysis in this paper.
Firstly, the nearest neighbor for data association (NNDA) and fast algorithm for data association (FAFDA) are introduced in this paper. At the same time, emulation experiment and analysis of emulation result are conducted, which validates merits and defects between NNDA and FAFDA. This paper presents a novel method of data association based on fast algorithm for data association and multiple sensors joint probability data association, which is called multiple sensors fast algorithm for data association (MSFAFDA). The basic theory of MSFAFDA is that all the data in gate are regarded as valid data. But associated probabilities of the valid data are different. The second data are calculated according to the first data and observational data. At the same time, emulation result and error contrast and analysis between NNDA and MSFAFDA validate it.
Secondly, in allusion to question which the precision of targets gained by radar is low, azimuth gained by infrared sensor with high precision is introduced to improve azimuth precision of targets fused. This algorithm makes good use of merits between radar and infrared sensor. Emulation experiment validates that azimuth error of targets fused is reduced rapidly.
引文
1刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].国防工业出版社,1998
2何友,王国宏,陆大金,彭应宁.多传感器信息融合及应用[M].电子工业版,2000
3扬万海.多传感器数据融合及应用[M].西安电子科技大学出版社,2004
4 Lawrence A. Klein多传感器数据融合理论及应用[M].戴亚平,刘征,郁光辉.第二版.北京理工大学出版社,2004
5王宏飞.被动传感器信息融合研究与应用[D].中科院博士论文.2003
6王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策.2001,16(5)：518-522
7胡丹丹,肖书明,王燕清,高庆吉.基于多传感器的数据融合技术[J].东北电力学院学报.2004,24(1)：62-67
8刘高峰,郑伟新.分布综合式数据融合模式及性能评估问题研究[J].舰船电子工程.1998,2：17-22
9高翔,王勇.数据融合技术综述[J].计算机测量与控制.2002,10(11)：706-709
10徐毅,金德琨,敬忠良.数据融合研究的回顾与展望[J].信息与控制.2002,31(3)：251-255
11艾利,惠丽克.对国外军用多传感器数据融合系统的分析[J].情报指挥控制系统与仿真技术.1998,9：8-16
12何友.多传感器综合系统中的数据合成.全国C3I年会论文.1988,11.郑州.
13何友.多传感器系统中的航迹合成[J].火力与指挥控制.1990,1：1-6
14胡文龙,毛士艺.多传感器多目标跟踪中的概率数据关联[J].电子学报1996,9：30-35
15 Wang Guohong, Mao Shiyi and He You. A Triple-threshold Radar-to-ESM Correlation Algorithm When Each Radar Track Being Specified by Different Number of Measurements[J]. IEEE Proceedings Radar, Sonar, and Navigation
16许录平.一种新的自适应a—β滤波方法[J].西安电子科技大学学报.1998,25(3)：7-13
17杨万海.多传感器航迹融合算法研究(八)[J].九五军事预言报告.2002
18李红艳.一种α—β和α—β—γ组合滤波方法[J].火控雷达技术.1995,(5)：6-9
19 Aziz A M A. New Data Fusion Algorithms for Distributed Multisensor Multitarget Environments [J]. NPSM -Dissertation.1999
20 Chang K C. A Distributed Multisensor Multitarget Tracking Algorithm [J]. SPIE.1995,2484
21 Saha R K. An Enfficient Algorithm for Multisensor Track Fusion [J]. IEEE Trans.on AES.1998, 34(1)
22 Klein L A. Millimeter-wave and infrared multisensor design and signal processing. Norwood, MA:Artech House,1997
23 Hall D L, Llinas J. An introduction to multisensor data fusion [J].Proceedings of the IEEE,1997, 85(I):6-23
24刘城霞,干宝树,乔向东.红外方位信息在航迹关联中的应用[J].红外技术2002,24(3)： 10-14
25谢希权,谢邦荣,李伟仁.机载雷达与红外搜索跟踪装置的航迹融合研究[J].系统工程与电子技术.2002,24(4)：2025
26贾成功,王错,王志刚,刘宗新.光电传感器角度信息关联技术[J]. 光电技术应用.2003,4：28-30
27王国宏,利用角度,角度变化率和ITG信息的主被动传感器数据关联[J].雷达科学与技术.2003,1(1)：39-45
28刘刚,刘明.多目标跟踪方法综述[J].电光与控制.2004,11(3)：26-29
29孟章荣.军事应用中的多源信息融合技术[J].现代防御技术.2001,29(2)：27-30
30宋小全,孙仲康.多传感器机动目标跟踪[J].电子学报.1997,25(9)：98-101
31刘城霞,王宝树.适于复杂信息融合系统的近似联合概率数据关联算法[J].电子与信息学报.2003,25(10)：1355-1360
32王国宏,毛士艺,何友.雷达和红外航迹关联性能的评估[J].电子与信息学报.2002,24(12)：1917-1924
33何友.分布式多传感器信息融合中的航迹文件管理技术[J].现代雷达.1999,25(6)：65-69
34杨海成,林国村.光电传感器在战场侦察车上的应用与发展[J].应用光学.2004.25(6)：43—46
35王宏强.目标融合跟踪技术及性能预测研究[D].国防科技大学博士论文.2002
36 Farina A Studer F A. Radar Data Processing [M].1985
37 Lee M, New Multitarget Data Association Using OSJPDA Algorithm For Automotive Radar[J]. IEICE Trance. Electron.2001, E84-c(8)
38 Welch G.. An Introduction to the Kalman Filter [J]. UNC-Chapel Hill,2002, TR95-041
39 Aidela V J.Kalman filter behavior in bearing-only tracking application [J]. IEEE Trans on AES,1979,14:29-30
2何友,王国宏,陆大金,彭应宁.多传感器信息融合及应用[M].电子工业版,2000
3扬万海.多传感器数据融合及应用[M].西安电子科技大学出版社,2004
4 Lawrence A. Klein多传感器数据融合理论及应用[M].戴亚平,刘征,郁光辉.第二版.北京理工大学出版社,2004
5王宏飞.被动传感器信息融合研究与应用[D].中科院博士论文.2003
6王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策.2001,16(5)：518-522
7胡丹丹,肖书明,王燕清,高庆吉.基于多传感器的数据融合技术[J].东北电力学院学报.2004,24(1)：62-67
8刘高峰,郑伟新.分布综合式数据融合模式及性能评估问题研究[J].舰船电子工程.1998,2：17-22
9高翔,王勇.数据融合技术综述[J].计算机测量与控制.2002,10(11)：706-709
10徐毅,金德琨,敬忠良.数据融合研究的回顾与展望[J].信息与控制.2002,31(3)：251-255
11艾利,惠丽克.对国外军用多传感器数据融合系统的分析[J].情报指挥控制系统与仿真技术.1998,9：8-16
12何友.多传感器综合系统中的数据合成.全国C3I年会论文.1988,11.郑州.
13何友.多传感器系统中的航迹合成[J].火力与指挥控制.1990,1：1-6
14胡文龙,毛士艺.多传感器多目标跟踪中的概率数据关联[J].电子学报1996,9：30-35
15 Wang Guohong, Mao Shiyi and He You. A Triple-threshold Radar-to-ESM Correlation Algorithm When Each Radar Track Being Specified by Different Number of Measurements[J]. IEEE Proceedings Radar, Sonar, and Navigation
16许录平.一种新的自适应a—β滤波方法[J].西安电子科技大学学报.1998,25(3)：7-13
17杨万海.多传感器航迹融合算法研究(八)[J].九五军事预言报告.2002
18李红艳.一种α—β和α—β—γ组合滤波方法[J].火控雷达技术.1995,(5)：6-9
19 Aziz A M A. New Data Fusion Algorithms for Distributed Multisensor Multitarget Environments [J]. NPSM -Dissertation.1999
20 Chang K C. A Distributed Multisensor Multitarget Tracking Algorithm [J]. SPIE.1995,2484
21 Saha R K. An Enfficient Algorithm for Multisensor Track Fusion [J]. IEEE Trans.on AES.1998, 34(1)
22 Klein L A. Millimeter-wave and infrared multisensor design and signal processing. Norwood, MA:Artech House,1997
23 Hall D L, Llinas J. An introduction to multisensor data fusion [J].Proceedings of the IEEE,1997, 85(I):6-23
24刘城霞,干宝树,乔向东.红外方位信息在航迹关联中的应用[J].红外技术2002,24(3)： 10-14
25谢希权,谢邦荣,李伟仁.机载雷达与红外搜索跟踪装置的航迹融合研究[J].系统工程与电子技术.2002,24(4)：2025
26贾成功,王错,王志刚,刘宗新.光电传感器角度信息关联技术[J]. 光电技术应用.2003,4：28-30
27王国宏,利用角度,角度变化率和ITG信息的主被动传感器数据关联[J].雷达科学与技术.2003,1(1)：39-45
28刘刚,刘明.多目标跟踪方法综述[J].电光与控制.2004,11(3)：26-29
29孟章荣.军事应用中的多源信息融合技术[J].现代防御技术.2001,29(2)：27-30
30宋小全,孙仲康.多传感器机动目标跟踪[J].电子学报.1997,25(9)：98-101
31刘城霞,王宝树.适于复杂信息融合系统的近似联合概率数据关联算法[J].电子与信息学报.2003,25(10)：1355-1360
32王国宏,毛士艺,何友.雷达和红外航迹关联性能的评估[J].电子与信息学报.2002,24(12)：1917-1924
33何友.分布式多传感器信息融合中的航迹文件管理技术[J].现代雷达.1999,25(6)：65-69
34杨海成,林国村.光电传感器在战场侦察车上的应用与发展[J].应用光学.2004.25(6)：43—46
35王宏强.目标融合跟踪技术及性能预测研究[D].国防科技大学博士论文.2002
36 Farina A Studer F A. Radar Data Processing [M].1985
37 Lee M, New Multitarget Data Association Using OSJPDA Algorithm For Automotive Radar[J]. IEICE Trance. Electron.2001, E84-c(8)
38 Welch G.. An Introduction to the Kalman Filter [J]. UNC-Chapel Hill,2002, TR95-041
39 Aidela V J.Kalman filter behavior in bearing-only tracking application [J]. IEEE Trans on AES,1979,14:29-30