基于偏差映射聚类的目标关联方法
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摘要
多传感器目标关联是确定不同传感器系统观测的若干信号是否来源于同一目标,它是现代多传感器系统中的一个重要问题。传统的关联方法通过计算不同观测间的关联代价,通过求解代价矩阵最优解来获得关联匹配,但是容易受到环境和传感器性能的影响。提出了一种基于偏差映射聚类(bias mapping cluster,BMC)的目标关联方法,通过对多个传感器间观测目标偏差映射点进行聚类,搜索局部密度最大的映射点集作为传感器间的目标关联结果,走出了利用数学方法求解全局最优解的传统模式。相较其他传感器间目标关联方法,仿真结果表明该方法能有效利用目标观测的空间散布特性,关联正确率更高,并对虚假目标和目标失配等情况具有更强的适应性。
Multi-sensor target association is to determine whether some signals observed by different sensor systems originate from the same target or not. It is an important problem in modern multi-sensor systems. The traditional method is usually to calculate the correlation cost matrix,use optimal algorithm to solve matrix and get target correlation pairs between different sensors,which is easily influenced by the environment and sensor performance. We propose a target association method based on bias mapping clustering( BMC). The mesurement bias pair is mapped into a point in the two-dimensional space and these points are clustered. By searching the local point density peak as the target correlation results between sensors,the traditional mode of using mathematical methods to solve the global optimal solution is gotten out. Compared to other methods,the simulation results show that this method can effectively use the spatial distribution of target observations to improve the association accuracy,and has a robust performance at the situation of false target and target mismatch.
Multi-sensor target association is to determine whether some signals observed by different sensor systems originate from the same target or not. It is an important problem in modern multi-sensor systems. The traditional method is usually to calculate the correlation cost matrix,use optimal algorithm to solve matrix and get target correlation pairs between different sensors,which is easily influenced by the environment and sensor performance. We propose a target association method based on bias mapping clustering( BMC). The mesurement bias pair is mapped into a point in the two-dimensional space and these points are clustered. By searching the local point density peak as the target correlation results between sensors,the traditional mode of using mathematical methods to solve the global optimal solution is gotten out. Compared to other methods,the simulation results show that this method can effectively use the spatial distribution of target observations to improve the association accuracy,and has a robust performance at the situation of false target and target mismatch.
引文
[1] PAN Quan,CHENG Yong-mei,LIANG Yan,et al. MultiSourse Information Fusion Theroy and Its Applications[M]. Beijing:Tsinghua University Press,2013.
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[8]柳鹏,高杰,刘扬.基于拍卖算法的目标分配问题优化[J].兵工自动化,2008,27(9):22-24.LIU Peng,GAO Jie,LIU Yang. Target Distribution Optimization Based on Auction Algorithm[J]. Ordnance Industry Automation,2008,27(9):22-24.
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[11] KATHERINE A R. Use of the Auction Algorithm for Target Object Mapping[R]. MIT Lincoln Laboratory,Lexington,Mass,Technical Report TR-1044,1998.
[12] DONG H L. Radar and Infrared Sensors Data Association at Different Locations[J]. Modern Electronic Technology,VOL. 8. 2014.
[13] YU Z H. Sensor Data Association Using Relative Positions Among Targets and Bias Estimation Between Separate Sensors[J]. Journal of University of Science and Technology of China,2014,44(1):34-42.
[14] ROECKER J,NGUYEN E. Fast Target Object Map Handover[J]. IEEE Transactions on Aerospace and Electronic Systems,2015,50(1):768-773.
[15] Rodriguez A,LAIO A. Clustering by Fast Rearch and Find of Density peaks[J]. Science,2014,344(6191):1492-1496.
[2] STONE L D,BARLOW C A,CORWIN T L. Bayesian Multiple Target Tracking[M]. Norwood,MA:Artech House,1999.
[3] WALZ E L,LLINSA J. Multi-Sensor Data Fusion[M].Boston,USA:Artech House,1990.
[4] KONSTANTINOVA P,UDVAREV A,SEMERDJIEV T.A Study of a Target Tracking Algorithm of Global Nearest Neighbor Approach[C]∥Proceedings of the Intemational Bnference on Bmputer Systems and Technologies. New York,USA,2003,E-line.
[5] FERRY J P. Exact Bias Removal for the Track-Track association Problem[C]∥The 12th International Conference on Infomation Fusion. Seattle,USA:IEEE Press,2009:1642-1649.
[6] LI X R. Compatibility and Modeling of Constrained Dynamic Systems[C]∥19th International Conference on Information Fusion,July 2016:240-247.
[7] BERTSEKAS D P,David Castanon. A Forward/Reverse Auction Algorithm for A Symmetric Assignment Problems[J] Computational Optimization and Applications,1992,1(3):277-97.
[8]柳鹏,高杰,刘扬.基于拍卖算法的目标分配问题优化[J].兵工自动化,2008,27(9):22-24.LIU Peng,GAO Jie,LIU Yang. Target Distribution Optimization Based on Auction Algorithm[J]. Ordnance Industry Automation,2008,27(9):22-24.
[9] SCHUTZ R,ENGELLBERG B,MCALLISTER R,et al.Maneuver Tracking Algorithms for A New Target Tracking Applications[C]∥Drummond O(ed.),Signal and Data Processing of Small Targets,SPIE3809,1999:308-319.
[10] Levedahl. An Explicit Pattem Matching Assigment Algorithm[C]∥Proceedings of Intermtional Society for Optical Engineering. Culver,USA:SPIE Press,2002:461-469.
[11] KATHERINE A R. Use of the Auction Algorithm for Target Object Mapping[R]. MIT Lincoln Laboratory,Lexington,Mass,Technical Report TR-1044,1998.
[12] DONG H L. Radar and Infrared Sensors Data Association at Different Locations[J]. Modern Electronic Technology,VOL. 8. 2014.
[13] YU Z H. Sensor Data Association Using Relative Positions Among Targets and Bias Estimation Between Separate Sensors[J]. Journal of University of Science and Technology of China,2014,44(1):34-42.
[14] ROECKER J,NGUYEN E. Fast Target Object Map Handover[J]. IEEE Transactions on Aerospace and Electronic Systems,2015,50(1):768-773.
[15] Rodriguez A,LAIO A. Clustering by Fast Rearch and Find of Density peaks[J]. Science,2014,344(6191):1492-1496.