多源置信综合的雷达与ESM协同跟踪方法
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摘要
跟踪空间邻近目标时,仅依靠运动学信息不足以实现可靠的数据关联,而基于动目标指示器(Moving Target Indicator,MTI)雷达和电子支援措施(Electronic Support Measurement,ESM)的多源异类传感器数据融合可以通过提升数据关联性能达到改善跟踪性能的目的。通过构建基于五种成比例再分配规则(Five Proportional Conflict Redistribution Rules,PCR5)置信度量的数据关联策略,将目标运动学信息和属性信息结合做多特征推理,解决异类传感器数据的不确定性和不一致性;利用Dempster-Shafer(DS)证据理论方法进行属性融合更新,完成属性信息在时间序列上的相干积累,实现空间邻近目标的可靠跟踪。该方法从数据关联和状态估计两方面联合进行改进,通过引入属性信息提升数据关联的正确性,从而提升跟踪性能,实现多源异类信息下的协同跟踪。仿真表明,相比于仅雷达跟踪、雷达和ESM序惯跟踪等方案,该方法可有效提升跟踪精度和关联性能。
In tracking closely-spaced targets,the kinematic information is not enough to solve the problem of track interleaving caused by the misuse of measurement. Data fusion of heterogeneous sensors such as moving target indicator( MTI) radar and electronic support measurement( ESM) is the way to solve the problem.In this paper,a data association strategy based on five proportional conflict redistribution rules( PCR5) confidence measure is constructed,which combines kinematics information with attribute information for multi-feature reasoning to solve the uncertainty and inconsistency of heterogeneous sensor data.The Dempster-Shafer( DS) evidence theory is used to complete the coherent accumulation of attribute information in time series.This method improves the accuracy of data association by introducing attribute information,thus realizing the cooperative tracking under multi-source heterogeneous information. Simulation results show that tracking accuracy and data association performance are both improved effectively.
In tracking closely-spaced targets,the kinematic information is not enough to solve the problem of track interleaving caused by the misuse of measurement. Data fusion of heterogeneous sensors such as moving target indicator( MTI) radar and electronic support measurement( ESM) is the way to solve the problem.In this paper,a data association strategy based on five proportional conflict redistribution rules( PCR5) confidence measure is constructed,which combines kinematics information with attribute information for multi-feature reasoning to solve the uncertainty and inconsistency of heterogeneous sensor data.The Dempster-Shafer( DS) evidence theory is used to complete the coherent accumulation of attribute information in time series.This method improves the accuracy of data association by introducing attribute information,thus realizing the cooperative tracking under multi-source heterogeneous information. Simulation results show that tracking accuracy and data association performance are both improved effectively.
引文
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[2] HONG L,CONG S,PRONOBIS M T,et al.Wavelets feature aided tracking using GMTI/HRR data[J]. Signal Processing,2003,83(12):2683-2690.
[3] ANGELOVA D,MIHAYLOVA L.Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information[C]//Proceedings of the 7th International Conference on Information Fusion.Stockolm:IEEE,2004:709-716.
[4] JI Q,LAN J,LI X R.Joint fault detection,identification,and state estimation based on conditional joint decision and estimation[C]//Proceedings of 19th International Conference on Information Fusion. Heidelberg:IEEE,2016:1147-1154.
[5] LI X R. Optimal Bayes joint decision and estimation[C]//Proceedings of the 10th International Conference on Information Fusion.Quebec City:IEEE,2007:1-8.
[6] CAO W,LAN J,LI X R. Extended object tracking and classification using radar and ESM sensor data[J]. IEEE Signal Processing Letters,2018,25(1):90-94.
[7] JI Q,LAN J,LI X R.Joint tracking for capturing and classification based on joint decision and estimation[C]//Proceedings of 21st International Conference on Information Fusion.Cambridge:IEEE,2018:2121-2128.
[8] DULEK B.A restricted Bayes approach to joint detection and estimation under prior uncertainty[J].IEEE Transactions on Aerospace and Electronic Systems,2018,54(4):1767-1782.
[9] BOERS Y,DRIESSEN H.Integrated tracking and classification:an application of hybrid state estimation[C]//Proceedings of SPIE Conference on Signal and Data Processing of Small Targets.San Diego:IEEE,2001:198-209.
[10] CHALLA S,PULFORD G W. Joint target tracking and classification using radar and ESM sensors[J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37(3):1039-1055.
[11] HERMAN S M. Joint passive radar tracking and target classification using radar cross section[C]//Proceedings of SPIE Conference on Signal and Data Processing of Small Targets.San Diego:IEEE,2003:402-417.
[12] LIU Z G,PAN Q,DEZERT J,et al.Classifier fusion with contextual reliability evaluation[J]. IEEE Transactions on Cybernetics,2018,48(5):1605-1618.
[13]刘准钆,潘泉,DEZERT J,等.不确定数据信任分类与融合[M].北京:科学出版社,2016.
[14] SMARANDACHE F,DEZERT J.Proportional conflict redistribution rules for information fusion[J]. Acta Endocrinologica,2006,100(4):613-22.
[15] LIU Z G,PAN Q,MERCIER G,et al.A new incomplete pattern classification method based on evidential reasoning[J]. IEEE Transactions on Cybernetics,2017,45(4):635-646.