雷达组网误差配准算法研究
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摘要
数据配准是雷达组网信息融合的重要环节,其核心在于雷达系统误差估计。目前已有的系统误差估计算法大多存在模型简单、约束条件多等不足,最关键的是它们对可观测问题考虑较少。然而理论与实践证明,可观测度对于系统误差估计算法性能的影响是不可忽视的,某些情况下甚至是决定性的。
论文围绕可观测度展开分析,重点解决低可观测度条件下的系统误差估计问题。主要工作如下:
(一)针对可观测度对系统误差估计精度的影响难以定量描述的问题,建立雷达组网误差估计模型,提出基于可观测度检测的系统误差估计算法。根据分析,采用目标真实航迹数据和雷达观测数据构建的系统误差估计模型的可观测度是近似相等的,以此为依据,本文通过间接检测的方法评价出系统的可观测度,解决了上述问题。仿真实验表明,算法整体性能优良。
(二)低可观测度条件下,观测数据分布不平衡、信息矩阵条件数增大会使噪声对系统误差估计的影响加剧,进而导致估计值发散。提出基于低通滤波预处理的系统误差估计算法,对原观测集进行预处理,最大限度地滤除数据中的噪声,由此提高系统误差估计精度。仿真实验表明,算法性能较已有算法显著改善。
Data registration is an important part of information fusion in radar-network. The key to data registration is radar system errors estimation. The disadvantages of most existing algorithms for radar system errors estimation include relatively simple models, too many constraints and so on. The most important point is that the observability problem is seldom considered in the algorithms. According to some relative research results, however, it is proved that the influence of the observability cannot be ignored in system errors estimation for it may determine the accuracy of the results in certain circumstances.
The thesis analyses the observability and focuses on the system errors estimation problem in low observability circumstances. The main contributions can be summarized as follows:
(1) The influence of the observability on the precision of the system errors estimation is difficult to be described quantitatively. Aiming at the problem, a model for the radar-network errors estimation is established and a system errors estimation algorithm based on the detection of the observability is proposed. It can be concluded by theoretic analysis that the observability of the system errors estimation model established with the true track data and the observed data from radars separately is approximately the same. Based on the fact the observability of the system is evaluated by an indirect detection method. Simulation experiments show that the performance of the whole algorithm is good.
(2) The imbalance of the distribution of the observation data and the increase of the condition number of the information matrix enhance the influence of the noise on system errors estimation in low observability circumstances which may result in the divergence of the estimation. To solve the problem, a system errors estimation algorithm is proposed which is based on low-pass filtering. The noise within the original observation set is removed by the preprocessing which enhances the precision of the system errors estimation. Experimental results show that the performance of the existing algorithm has been improved greatly.
论文围绕可观测度展开分析,重点解决低可观测度条件下的系统误差估计问题。主要工作如下:
(一)针对可观测度对系统误差估计精度的影响难以定量描述的问题,建立雷达组网误差估计模型,提出基于可观测度检测的系统误差估计算法。根据分析,采用目标真实航迹数据和雷达观测数据构建的系统误差估计模型的可观测度是近似相等的,以此为依据,本文通过间接检测的方法评价出系统的可观测度,解决了上述问题。仿真实验表明,算法整体性能优良。
(二)低可观测度条件下,观测数据分布不平衡、信息矩阵条件数增大会使噪声对系统误差估计的影响加剧,进而导致估计值发散。提出基于低通滤波预处理的系统误差估计算法,对原观测集进行预处理,最大限度地滤除数据中的噪声,由此提高系统误差估计精度。仿真实验表明,算法性能较已有算法显著改善。
Data registration is an important part of information fusion in radar-network. The key to data registration is radar system errors estimation. The disadvantages of most existing algorithms for radar system errors estimation include relatively simple models, too many constraints and so on. The most important point is that the observability problem is seldom considered in the algorithms. According to some relative research results, however, it is proved that the influence of the observability cannot be ignored in system errors estimation for it may determine the accuracy of the results in certain circumstances.
The thesis analyses the observability and focuses on the system errors estimation problem in low observability circumstances. The main contributions can be summarized as follows:
(1) The influence of the observability on the precision of the system errors estimation is difficult to be described quantitatively. Aiming at the problem, a model for the radar-network errors estimation is established and a system errors estimation algorithm based on the detection of the observability is proposed. It can be concluded by theoretic analysis that the observability of the system errors estimation model established with the true track data and the observed data from radars separately is approximately the same. Based on the fact the observability of the system is evaluated by an indirect detection method. Simulation experiments show that the performance of the whole algorithm is good.
(2) The imbalance of the distribution of the observation data and the increase of the condition number of the information matrix enhance the influence of the noise on system errors estimation in low observability circumstances which may result in the divergence of the estimation. To solve the problem, a system errors estimation algorithm is proposed which is based on low-pass filtering. The noise within the original observation set is removed by the preprocessing which enhances the precision of the system errors estimation. Experimental results show that the performance of the existing algorithm has been improved greatly.
引文
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[2]吴小飞,刘晓晶.对雷达组网数据融合中几个关键问题的研究.现代雷达,V01.26,No.3,2004:29-32.
[3]彭焱,徐毓,金宏斌.多传感器数据融合系统中时间配准算法分析.雷达与对抗,No.2,2005:16-19.
[4]江红,张炎华,赵忠华.多传感器信息融合的时间不确定性.上海交通大学学报,V01.39,No.3,2005:366-372.
[5]杨宏文.多传感器目标跟踪理论与技术研究.工学博士学位论文.长沙,国防科技大学研究生院,1998.
[6]A.Farina,F.A.Studer.Radar Data Processing(Vol.Ⅱ).Research Studies Press LTD,1985.
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[21]金宏斌,徐毓,万仕保.基于强跟踪滤波器的多雷达配准方法.火力与指挥控制,V01.30,No.1,2005:85-88.
[22]文成林,陈志国,周东华.基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计.电子学报,V01.30,No.11,2002:1715-1717.
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[25]R.E.Helmick,T.R.Rice.Removal of alignment errors in an integrated system of two 3-D sensors.IEEE Transactions on Aerospace and Electronic Systems,Vol.29,No.4,1993:1335-1343.
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[33]周宏仁,敬忠良,王培德.机动目标跟踪.北京,国防工业出版社,1991.
[34]Mounir Ben Ghalia,Ali T.Alouani.Observability requirements for passive target tracking.IEEE,0094-2898/93,1993:253-257.
[35]Taek Lyul Song.Observability of Target Tracking with Bearings-only Measurements.IEEE Transactions on Aerospace and Electronic Systems,Vol.32,No.4,1996:1468-1472.
[36]D.van Huyssteen,M.Farooq.Performance analysis of Bearings-only Tracking Algorithm.SPIE Conference on Acquisition,Tracking and Pointing,Vol.3365,1998:139-149.
[37]孙仲康,周一宇,何黎星.但多基地有源无源定位技术.北京,国防工业出版社,1996.
[38]李盾.空间预警系统对目标的定位与预报.工学博士学位论文.长沙,国防科技大学研究生院,2001.
[39]周一宇,孙仲康.雷达被动探测定位的可观测性.电子学报,Vol.22,No.3,1994:51-57.
[40]刘勋,相敬林.基于声强的水中体积目标被动定位的可观测性分析.西北工业大学学报,Vol.20,No.2,2002:248-251.
[41]冯道旺,李宗华,周一宇,孙仲康.一种单站无源定位方法及其可观测性分析.国防科技大学学报,Vol.26,No.1,2004:68-71.
[42]宁晓琳,房建成.航天器自主天文导航系统的可观测性及可观测度分析.北京航空航天大学学报,Vol.31,No.6,2005:673-677.
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[2]吴小飞,刘晓晶.对雷达组网数据融合中几个关键问题的研究.现代雷达,V01.26,No.3,2004:29-32.
[3]彭焱,徐毓,金宏斌.多传感器数据融合系统中时间配准算法分析.雷达与对抗,No.2,2005:16-19.
[4]江红,张炎华,赵忠华.多传感器信息融合的时间不确定性.上海交通大学学报,V01.39,No.3,2005:366-372.
[5]杨宏文.多传感器目标跟踪理论与技术研究.工学博士学位论文.长沙,国防科技大学研究生院,1998.
[6]A.Farina,F.A.Studer.Radar Data Processing(Vol.Ⅱ).Research Studies Press LTD,1985.
[7]I.Jonsdottir,A.S.Hauksdottir.Integrity Monitoring and Estimation of Systematic Errors in Radar Data Systems.In Proc.IEEE Int.Radar Conf.,1995:310-316.
[8]H-T.Ong,B.Ristic,M.Oxenham.Sensor Registration Using Airlines.In Proceedings of the 5th International Conference on Information Fusion,Annapolis,MD,2002.
[9]J.J.Burke.The SAGE real quality control fraction and its interface with BUIC Ⅱ/BUIC Ⅲ.Technical report 308,MITRE Corporation,1966.
[10]M.P.Dana.Registration:A prerequisite for multiple sensor tracking.In Y.Bar-Shalom (Ed.),Multitarget-Multisensor Tracking:Advanced Applications.Dedham,MA:Artech House,1990.
[11]Yifeng Zhou,Henry Leung,Member,IEEE,Patrick C.Yip.An exact maximum likelihood registration algorithm for data fusion.IEEE Transactions on Signal Processing,Vol.45,No.6,1997:1560-1573.
[12]Yifeng Zhou,Henry Leung,Member,IEEE,Martin Blanchette.Sensor alignment with Earth-Centered Earth-Fixed(ECEF) coordinate system.IEEE Transactions on Aerospace and Electronic Systems,Vol.35,No.2,1999:410-418.
[13]N.Okello,B.Ristic.Maximum likelihood registration for multiple dissimilar sensors.IEEE Transactions on Aerospace and Electronic Systems,Vol.39,No.3,2003:1074 -1083.
[14]Nassib Nabaa,Robert H.Bishop.Solution to a Multisensor Tracking Problem with Sensor Registration Errors.IEEET-AES,Vol.35,No.1,1999:354-363.
[15]Yifeng Zhou.A Kalman Filter based registration approach for asynchronous sensors in multiple sensor fusion applications.IEEE,ICASSP,2004:293-296.
[16]Haim Karmiely,T S.Hava.Sensor Registration Using Neural Network.IEEE T-AES,Vol.36,No.1,2000:85-100.
[17]王波,王灿林,董云龙.RTQC误差配准算法性能分析.系统仿真学报,Vol.18,No.11,2006:3067-3069.
[18]董云龙,何友,王国宏,李东.一种改进的雷达组网误差配准算法.系统仿真学报,Vol.17,No.7,2005:1583-1586.
[19]王建卫.基于模拟退火算法的组网雷达系统误差校正.现代雷达,Vol.28,No.8,2006:4-8.
[20]董云龙,何友,王国宏,于占仁,王瑞友.基于ECEF的广义最小二乘误差配准技术.航空学报,V01.27,No.3,2006:463-467.
[21]金宏斌,徐毓,万仕保.基于强跟踪滤波器的多雷达配准方法.火力与指挥控制,V01.30,No.1,2005:85-88.
[22]文成林,陈志国,周东华.基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计.电子学报,V01.30,No.11,2002:1715-1717.
[23]芮国胜,康健.基于双卡尔曼滤波的系统偏差消除方法.系统工程与电子技术,V01.27,No.2,2005:234-236.
[24]张高煜,赵恒,杨万海.模糊C均值聚类在多传感器数据配准中的应用.电子对抗技术,Vol.20 No.1,2005:24-28.
[25]R.E.Helmick,T.R.Rice.Removal of alignment errors in an integrated system of two 3-D sensors.IEEE Transactions on Aerospace and Electronic Systems,Vol.29,No.4,1993:1335-1343.
[26]P.F.Easthope.Observability of sensor biases using multiple track reports.SPIE Conference on Signal and Data Processing of Small Targets,1999:332-343
[27]T.Boukhobza,F.Hamelin,S.Martinez-Martinez.State and input observability for structured linear systems:Agraph-theoretic approach.Automatica 43,2007:1204-1210.
[28]H.Leung,M.Blanchette,C.Harrison.A least squares fusion of multiple radar data.In Proceedings of RADAR '94,1994:364-369.
[29]H.Abbas,D.P.Xue,M.Farooq,G.Parkinson,M.Blanchette.Track-independent estimation schemes for registration in a network of sensors.Proceedings of the 35th Conference on Decision and Control,IEEE,Kobe,Japan,1996:2563-2568.
[30]Steven M.Key.Fundamental of Statistical Signal Processing.北京,电子工业出版社,2003.
[31]刘福生,罗鹏飞.统计信号处理.长沙,国防科技大学出版社,1999.
[32]单振兴.目标无源定位与跟踪算法研究.工学硕士学位论文.长沙,国防科技大学研究生院,2005.
[33]周宏仁,敬忠良,王培德.机动目标跟踪.北京,国防工业出版社,1991.
[34]Mounir Ben Ghalia,Ali T.Alouani.Observability requirements for passive target tracking.IEEE,0094-2898/93,1993:253-257.
[35]Taek Lyul Song.Observability of Target Tracking with Bearings-only Measurements.IEEE Transactions on Aerospace and Electronic Systems,Vol.32,No.4,1996:1468-1472.
[36]D.van Huyssteen,M.Farooq.Performance analysis of Bearings-only Tracking Algorithm.SPIE Conference on Acquisition,Tracking and Pointing,Vol.3365,1998:139-149.
[37]孙仲康,周一宇,何黎星.但多基地有源无源定位技术.北京,国防工业出版社,1996.
[38]李盾.空间预警系统对目标的定位与预报.工学博士学位论文.长沙,国防科技大学研究生院,2001.
[39]周一宇,孙仲康.雷达被动探测定位的可观测性.电子学报,Vol.22,No.3,1994:51-57.
[40]刘勋,相敬林.基于声强的水中体积目标被动定位的可观测性分析.西北工业大学学报,Vol.20,No.2,2002:248-251.
[41]冯道旺,李宗华,周一宇,孙仲康.一种单站无源定位方法及其可观测性分析.国防科技大学学报,Vol.26,No.1,2004:68-71.
[42]宁晓琳,房建成.航天器自主天文导航系统的可观测性及可观测度分析.北京航空航天大学学报,Vol.31,No.6,2005:673-677.
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