系统偏差条件下的航迹相关技术研究
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摘要
航迹相关是多传感器融合跟踪系统的关键技术之一,系统偏差的存在使航迹相关问题变得更为复杂。传统的处理方式是先估计出系统误差的值,然后再进行航迹相关。论文重点研究在不估计出系统误差具体数值的情况下直接进行航迹相关的方法,主要内容如下:
1、研究了基于历史信息的航迹相关方法。由于系统误差等因素的影响,不同传感器观测到的同一个目标的航迹之间存在一定的位置偏差。这一位置偏差的取值范围可作为航迹相关的初步判定依据。经过一段时间的跟踪后,可以建立航迹间的位置偏差序列。经分析,位置偏差序列的样本方差存在理论最大值,基于样本方差理论最大值和实际样本方差的比值,提出了基于历史信息的航迹相关方法。
2、将航迹看成是空间中的点模式,利用图谱理论和灰色理论,研究了基于航迹空间分布信息的航迹相关方法。首先研究了归一化Laplace矩阵作为表示矩阵的图谱相关算法;然后研究了一种利用局部传感器观测到的航迹数据构建灰色自关联矩阵的灰色相关方法,该方法在有效利用航迹空间分布信息的基础上,可以将不同测度的属性信息融入到航迹相关过程中来,具有一定的实用价值。
3、论文最后研究了将航迹历史信息和空间分布信息结合起来的航迹相关算法,仿真实验表明,相比利用单一信息的相关方法,该方法性能更好。
Track correlation is one of the key technologies in multisensor-multitarget tracking and fusion system. However, the existence of system bias made the track correlation problem more difficult to solve. The common technologies deal this problem with classical track correlation method after estimating the system bias. This thesis concentrates on the novel track correlation method without estimating the system bias. The main contribution can be summarized as follows:
1. A track correlation method based on history information is researched in chapter2 Tracks of one target measured by different sensors in different location have position errors which is caused by coordinate conversion with system bias. By deducing the bounds of position errors, the coarse correlation method is proposed. It is demonstrated in the thesis that there exists a theoretical maximum variance of position errors sequence which is obtained by time accumulation. Depending on the empiric to theoretical variance ratio, the precision correlation method for track correlation is presented.
2. Tracks can be regarded as the point pattern in space at different times, so the graph theory and the gray theory can be introduced for track correlation based on the distributed space information. The generalized Laplace matrix is researched in chapter 3 for track correlation, and a correlation method which constructs the gray correlation matrix by local sensor measures is proposed. This novel method can use the distributed space information sufficiently and fusion the attribute information with different measurement conveniently.
3. The thesis presents a track correlation algorithm which is based on the combination of both the information of history and space. The simulation results demonstrate that the correlation performance is better than using just information of history or space.
1、研究了基于历史信息的航迹相关方法。由于系统误差等因素的影响,不同传感器观测到的同一个目标的航迹之间存在一定的位置偏差。这一位置偏差的取值范围可作为航迹相关的初步判定依据。经过一段时间的跟踪后,可以建立航迹间的位置偏差序列。经分析,位置偏差序列的样本方差存在理论最大值,基于样本方差理论最大值和实际样本方差的比值,提出了基于历史信息的航迹相关方法。
2、将航迹看成是空间中的点模式,利用图谱理论和灰色理论,研究了基于航迹空间分布信息的航迹相关方法。首先研究了归一化Laplace矩阵作为表示矩阵的图谱相关算法;然后研究了一种利用局部传感器观测到的航迹数据构建灰色自关联矩阵的灰色相关方法,该方法在有效利用航迹空间分布信息的基础上,可以将不同测度的属性信息融入到航迹相关过程中来,具有一定的实用价值。
3、论文最后研究了将航迹历史信息和空间分布信息结合起来的航迹相关算法,仿真实验表明,相比利用单一信息的相关方法,该方法性能更好。
Track correlation is one of the key technologies in multisensor-multitarget tracking and fusion system. However, the existence of system bias made the track correlation problem more difficult to solve. The common technologies deal this problem with classical track correlation method after estimating the system bias. This thesis concentrates on the novel track correlation method without estimating the system bias. The main contribution can be summarized as follows:
1. A track correlation method based on history information is researched in chapter2 Tracks of one target measured by different sensors in different location have position errors which is caused by coordinate conversion with system bias. By deducing the bounds of position errors, the coarse correlation method is proposed. It is demonstrated in the thesis that there exists a theoretical maximum variance of position errors sequence which is obtained by time accumulation. Depending on the empiric to theoretical variance ratio, the precision correlation method for track correlation is presented.
2. Tracks can be regarded as the point pattern in space at different times, so the graph theory and the gray theory can be introduced for track correlation based on the distributed space information. The generalized Laplace matrix is researched in chapter 3 for track correlation, and a correlation method which constructs the gray correlation matrix by local sensor measures is proposed. This novel method can use the distributed space information sufficiently and fusion the attribute information with different measurement conveniently.
3. The thesis presents a track correlation algorithm which is based on the combination of both the information of history and space. The simulation results demonstrate that the correlation performance is better than using just information of history or space.
引文
[1]何友,王国宏等.多传感器信息融合及应用[M].北京:电子工业出版社,2007,1-2.
[2]沈健.数据融合多目标跟踪方法研究[T].西北工业大学硕士学位论文,2003.14-17.
[3]杨宏文.多传感器目标跟踪理论与技术研究[D].国防科技大学工学博士学位论文,2000.
[4]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006,329-334.
[5]欧阳志宏.雷达组网误差配准算法研究[T].国防科学技术大学硕士学位论文.2007.
[6]贺明科,王正明,朱矩波.传感器系统误差与目标航迹的分布式估计[J].中国科学(E辑),2003,33(3):257-263.
[7] A.Farina, F.A.Studer. Radar Data Processing (Vol.II) [R].Research Studies Press LTD, 1985.
[8] I.Jonsdottir,A.S.Hauksdottir.Integrity Monitoring and Estimation of Systematic Errors in Radar Data Systems[C].In Proc.IEEE Int.Radar Conf,1995:310-3l6.
[9] H-T.Ong,B.Ristic,M.Oxenham.Sensor Registration Using Airlines[C].In Proceedings of the 5th International Conference on Information Fusion, Annapo1is, MD,2002.
[10] J.J.Burke.The SAGE real quality control fraction and its interface with BUIC II/BUIC III[R].Technical report 308, MITRE Corporation, 1966.
[11] M.P.Dana.Registration: A prerequisite for multiple sensors tracking [M]. Y.Bar-Shalom(Ed), Multitarget-Multisensor Tracking: Advanced Applications. Dedham, MA: Artech House, 1990.
[12] Yifeng Zhou, Henry Leung, Member, IEEE, Patrick C Yip An maximum likelihood registration algorithm for data fusion[J].IEEE Transactions on Signal Processing, 1997,45(6):1560-1573.
[13] Yifeng Zhou, Henry Leung, Member, IEEE, Martin Blanchette. Sensor alignment with Earth-Centered Earth-Fixed (ECEF) coordinate system [J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2):410-418.
[14] Nassib Nabaa, Robert H.Bishop.Solution to a Multisensor Tracking Problem with Sensor Registration Errors [J].IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(1):354-363.
[15] R.A.Singer, A.J.Kanyuck.computer Control of Multiple Site Track Correlation [J]. Automation, 1971, 7:455-463.
[16] Y.Bar-Shalom,L.Campo.The Effect of the Common Process Noise on theTwo-sensor Fused-track covariance[J].IEEE Transactions on Aerospace and Electronic Systems,1986,22(6):803-805.
[17] Y.Bar-Shalom.On.the.Track-to-track.correlation.Problem[J].IEEE Transactions on Automatic Control, 1981, 26(2):571-572.
[18] M.Kasaka,S.Miyamoto,and H.Ihara.A Track Correlation Algorithm for Multisensor integration[C].Proceedings of the IEEE/AIAA 5th Digital Avionics Systems Conf,1983,10.3:1-8.
[19]何友,谭庆海,蒋蓉蓉.多传感器综合系统中的航迹相关算法[J].火力与指挥控制,1989(1):1-12.
[20]何友,彭应宁等.一种新的多目标多传感器航迹相关算法[J].清华大学学报.1997(9):108-113.
[21]何友等.多传感器数据融合中的两种新的航迹相关算法[J].电子学报,1997,25(9):10-14.
[22]何友,彭应宁等,分布式多传感器数据融合系统中的双门限航迹相关算法[J].电子科学学刊,1997(6):721-728.
[23]第一作者.一种系统偏差条件下的航迹关联方法[J].信号处理,2009,25(8A):483-486.
[24]石钥,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.
[25]杨哲,韩崇昭,李晨等.基于目标之间拓扑信息的数据关联方法[J].系统仿真学报,2008,20(9):2357-2360.
[26]张昌芳,杨宏文,胡卫东,郁文贤.基于空间分布特征的阵群目标数据关联算法[J].系统仿真学报,2008,20(22):6074-6082.
[27] Tiberio S.Caetano, Terry Caelli.Graphical Models and Point Pattern Matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2006,28(10):1646-1663.
[28] Tiberio S.Caetano, Terry Caelli.Approximating the problem, not the solution: An alternative view of point set matching [J] .Pattern Recognition. 2006, (39):552-561.
[29] Bar-Shalom Y, Li X R. Multitarget-multisensor tracking: Principles and techniques [M].New Orleans: University of New Orleans, 1995.
[30]余安喜,杨宏文,胡卫东等.基于相对系统误差估计的组网雷达点迹融合技术[J].系统工程与电子技术,2003,25(9):1045-1048.
[31]董云龙,何友,王国宏等.基于ECEF的广义最小二乘误差配准技术[J].航空学报,2006,27(3):463-467.
[32]董瑞.基于图谱理论的图像匹配和图像分割算法研究[T].安徽大学硕士学位论文.2007.
[33] Park S. H., Lee K. M., Lee S. U. A line feature matching technique based on an eigenvector approach [J]. Computer Vision and Image Understanding, 2000, 77(3): 263-283.
[34] Carcassoni M, Hancock E R. Spectral correspondence for point pattern matching [J]. Pattern Recognition, 2003, 36 (1):193-204.
[35] Wilson R. C., Hancock E. R., Luo B. Pattern vectors from algebraic graph theory [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(7):1112-1124.
[36] Tang J., Dong L., Wang N., Fan Y. A Laplacian spectral method for stereo correspondence [J]. Pattern Recognition Letters, 2007, 28(12): 1391-1399.
[37] Shapiro L S, Brady J M. Feature-based correspondence-An eigenvector approach [J].Image Vision Comput, 1992, 10(5):283~288.
[38]张昌芳,杨宏文,胡卫东等.一种用于点模式匹配的改进型谱方法[J].计算机工程,2009,35(2):10-12.
[39]王年,范益政,韦穗,梁栋.基于图的Laplace谱的特征匹配[J].中国图象图形学报,2006,11(3):332-336.
[40]邓聚龙.灰色控制系统[M].武汉:华中科技大学出版社,1997.
[41]依晓,关欣,何友.分布式多目标跟踪系统的灰色航迹关联模型[J].信号处理,2005,21(6):653-662.
[42]郭文艳,韩崇昭,雷明.基于EMD和灰关联技术的航迹关联方法[J].控制与决策,2008,23(7):803-807.
[2]沈健.数据融合多目标跟踪方法研究[T].西北工业大学硕士学位论文,2003.14-17.
[3]杨宏文.多传感器目标跟踪理论与技术研究[D].国防科技大学工学博士学位论文,2000.
[4]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006,329-334.
[5]欧阳志宏.雷达组网误差配准算法研究[T].国防科学技术大学硕士学位论文.2007.
[6]贺明科,王正明,朱矩波.传感器系统误差与目标航迹的分布式估计[J].中国科学(E辑),2003,33(3):257-263.
[7] A.Farina, F.A.Studer. Radar Data Processing (Vol.II) [R].Research Studies Press LTD, 1985.
[8] I.Jonsdottir,A.S.Hauksdottir.Integrity Monitoring and Estimation of Systematic Errors in Radar Data Systems[C].In Proc.IEEE Int.Radar Conf,1995:310-3l6.
[9] H-T.Ong,B.Ristic,M.Oxenham.Sensor Registration Using Airlines[C].In Proceedings of the 5th International Conference on Information Fusion, Annapo1is, MD,2002.
[10] J.J.Burke.The SAGE real quality control fraction and its interface with BUIC II/BUIC III[R].Technical report 308, MITRE Corporation, 1966.
[11] M.P.Dana.Registration: A prerequisite for multiple sensors tracking [M]. Y.Bar-Shalom(Ed), Multitarget-Multisensor Tracking: Advanced Applications. Dedham, MA: Artech House, 1990.
[12] Yifeng Zhou, Henry Leung, Member, IEEE, Patrick C Yip An maximum likelihood registration algorithm for data fusion[J].IEEE Transactions on Signal Processing, 1997,45(6):1560-1573.
[13] Yifeng Zhou, Henry Leung, Member, IEEE, Martin Blanchette. Sensor alignment with Earth-Centered Earth-Fixed (ECEF) coordinate system [J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2):410-418.
[14] Nassib Nabaa, Robert H.Bishop.Solution to a Multisensor Tracking Problem with Sensor Registration Errors [J].IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(1):354-363.
[15] R.A.Singer, A.J.Kanyuck.computer Control of Multiple Site Track Correlation [J]. Automation, 1971, 7:455-463.
[16] Y.Bar-Shalom,L.Campo.The Effect of the Common Process Noise on theTwo-sensor Fused-track covariance[J].IEEE Transactions on Aerospace and Electronic Systems,1986,22(6):803-805.
[17] Y.Bar-Shalom.On.the.Track-to-track.correlation.Problem[J].IEEE Transactions on Automatic Control, 1981, 26(2):571-572.
[18] M.Kasaka,S.Miyamoto,and H.Ihara.A Track Correlation Algorithm for Multisensor integration[C].Proceedings of the IEEE/AIAA 5th Digital Avionics Systems Conf,1983,10.3:1-8.
[19]何友,谭庆海,蒋蓉蓉.多传感器综合系统中的航迹相关算法[J].火力与指挥控制,1989(1):1-12.
[20]何友,彭应宁等.一种新的多目标多传感器航迹相关算法[J].清华大学学报.1997(9):108-113.
[21]何友等.多传感器数据融合中的两种新的航迹相关算法[J].电子学报,1997,25(9):10-14.
[22]何友,彭应宁等,分布式多传感器数据融合系统中的双门限航迹相关算法[J].电子科学学刊,1997(6):721-728.
[23]第一作者.一种系统偏差条件下的航迹关联方法[J].信号处理,2009,25(8A):483-486.
[24]石钥,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.
[25]杨哲,韩崇昭,李晨等.基于目标之间拓扑信息的数据关联方法[J].系统仿真学报,2008,20(9):2357-2360.
[26]张昌芳,杨宏文,胡卫东,郁文贤.基于空间分布特征的阵群目标数据关联算法[J].系统仿真学报,2008,20(22):6074-6082.
[27] Tiberio S.Caetano, Terry Caelli.Graphical Models and Point Pattern Matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2006,28(10):1646-1663.
[28] Tiberio S.Caetano, Terry Caelli.Approximating the problem, not the solution: An alternative view of point set matching [J] .Pattern Recognition. 2006, (39):552-561.
[29] Bar-Shalom Y, Li X R. Multitarget-multisensor tracking: Principles and techniques [M].New Orleans: University of New Orleans, 1995.
[30]余安喜,杨宏文,胡卫东等.基于相对系统误差估计的组网雷达点迹融合技术[J].系统工程与电子技术,2003,25(9):1045-1048.
[31]董云龙,何友,王国宏等.基于ECEF的广义最小二乘误差配准技术[J].航空学报,2006,27(3):463-467.
[32]董瑞.基于图谱理论的图像匹配和图像分割算法研究[T].安徽大学硕士学位论文.2007.
[33] Park S. H., Lee K. M., Lee S. U. A line feature matching technique based on an eigenvector approach [J]. Computer Vision and Image Understanding, 2000, 77(3): 263-283.
[34] Carcassoni M, Hancock E R. Spectral correspondence for point pattern matching [J]. Pattern Recognition, 2003, 36 (1):193-204.
[35] Wilson R. C., Hancock E. R., Luo B. Pattern vectors from algebraic graph theory [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(7):1112-1124.
[36] Tang J., Dong L., Wang N., Fan Y. A Laplacian spectral method for stereo correspondence [J]. Pattern Recognition Letters, 2007, 28(12): 1391-1399.
[37] Shapiro L S, Brady J M. Feature-based correspondence-An eigenvector approach [J].Image Vision Comput, 1992, 10(5):283~288.
[38]张昌芳,杨宏文,胡卫东等.一种用于点模式匹配的改进型谱方法[J].计算机工程,2009,35(2):10-12.
[39]王年,范益政,韦穗,梁栋.基于图的Laplace谱的特征匹配[J].中国图象图形学报,2006,11(3):332-336.
[40]邓聚龙.灰色控制系统[M].武汉:华中科技大学出版社,1997.
[41]依晓,关欣,何友.分布式多目标跟踪系统的灰色航迹关联模型[J].信号处理,2005,21(6):653-662.
[42]郭文艳,韩崇昭,雷明.基于EMD和灰关联技术的航迹关联方法[J].控制与决策,2008,23(7):803-807.