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基于WGS-84坐标系的空地雷达最小二乘配准算法 被引量:1

LS Registration Algorithm Based on WGS-84 Coordinate for Airborne and Ground-based Radars
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摘要 空地雷达组网能实现二者的优势互补,特别是增强低空目标的探测跟踪能力,但其前提是实现运动的空基雷达和静止的地基雷达的配准。为贴近实际,在WGS-84(World Geodetic System)坐标系下建立空地雷达异步变数据率观测模型,采用内插外推的方法对空地观测进行时间配准,在此基础上导出适合工程应用的最小二乘配准算法进行空间配准。仿真结果表明了该算法的有效性和可实现性。 The ability to detect low altitude targets is much enhanced by netting of airborne and ground-based radars.The premise is registration for moving airborne radar and static ground-based radars.An asynchronous and varying rate measurement model based on WGS-84 coordinate for airborne radar and ground-based radars is built.Interpolation and extrapolation algorithm is used to implement time registration.Least square algorithm fit for project was deduced for space registration in WGS-84 coordinate system.The results of simulation demonstrate the method is effective and realistic.
出处 《武汉理工大学学报》 CAS CSCD 北大核心 2012年第9期139-143,共5页 Journal of Wuhan University of Technology
基金 国家自然科学基金(61102168)
关键词 空地雷达 变数据率观测 最小二乘 配准 airborne and ground-based radars varying rate measurement least square registration
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参考文献11

  • 1Lin X,Bar-Shalom Y,Kirubarajan T.Multisensor-multitarget Bias Estimation for General Asynchronous Sensors[J].IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):899-921.
  • 2Lin X,Bar-Shalom Y,Kirubarajan T.Exact Multisensor Dynamic Bias Estimation with Local Tracks[J].IEEE Trans-actions on Aerospace and Electronic Systems,2004,40(2):576-590.
  • 3胡洪涛,敬忠良,胡士强.一种基于Unscented卡尔曼滤波的多平台多传感器配准算法[J].上海交通大学学报,2005,39(9):1518-1521. 被引量:15
  • 4徐毅,陈非,敬忠良,金德琨.基于扩展Kalman滤波的空基多平台多传感器数据配准和目标跟踪算法[J].信息与控制,2001,30(5):403-407. 被引量:11
  • 5Lai Zuomei.A Relative Bias Estimation Algorithm on Airborne Radar Networks[C] //ICEMI.Beijing:IEEE ConferencePublications,2009:685-689.
  • 6Dong Yun-long,He You,Wang Guo-hong.A Generalized Least Squares Registration with Earth-centered Earth-fixed(ECEF)Coordinate System[C] //The 3th International Conference on Computational Electromagnetic and Its Proceed-ings.Beijing:IEEE Conference Publications,2004:79-84.
  • 7谢振华,江晶,高岚,范雄华.机载雷达与地面雷达的最大似然配准算法[J].现代防御技术,2011,39(2):123-127. 被引量:1
  • 8Leung H,Blanchette M.A Least Squares Fusion of Multiple Radar Data[C] //Proceedings of RADAR 1994.Paris:IEEEConference Publications,1994:364-369.
  • 9Steven M K.Fundamentals of Statistical Signal Processing,VolumeⅡ:Detection Theory[M].[S.l.] :New JerseyPrentice Hell,1998.
  • 10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[J].航空学报,2006,27(3):463-467. 被引量:32

二级参考文献31

  • 1刘延峰,潘泉,杜自成.机载雷达目标搜索和跟踪中的坐标系问题[J].火力与指挥控制,2005,30(3):40-43. 被引量:8
  • 2胡洪涛,敬忠良,胡士强.一种基于Unscented卡尔曼滤波的多平台多传感器配准算法[J].上海交通大学学报,2005,39(9):1518-1521. 被引量:15
  • 3王波,董云龙,王灿林.粒子滤波在误差配准中的应用[J].现代防御技术,2007,35(2):84-88. 被引量:8
  • 4LAI Zuo-mei. A Relative Bias Estimation Algorithm onAirborne Radar Networks [C] // The Ninth InternationalConference on Electronic Measurement & Instruments.Chengdu, China. 2009 : 685-689.
  • 5Simon Julier, Jeffrey Uhlmann, Hugh F Durrant-Whyte.A New Method for the Nonlinear Transformation ofMeans and Convariances in Filters and Estimators [J].IEEE Transactions on Automatic Control, 2000, 45( 3 ):477-482.
  • 6LEUNG H, BLANCHETTE M. A Least Squares Fusionof Multiple Radar Data [C] // Proceedings of Radar,Paris, 1994:364-369.
  • 7Daniel W McMichael, Nickens N Okello. MaximumLikelihood Registration of Dissimilar Sensors[C]//Proceedings of Australian Data Fusion Symposium( ADFS-96), Adelaide, Australia, 1996,31-34.
  • 8Nickens Okello, Branko Ristic. Maximum LikelihoodRegistration for Multiple Dissimilar Sensors [J]. IEEETrans on AES, 2003,39 (3):1074-1083.
  • 9JIANG Jing, YUAN Jun-quan, MA Xiao-yan, et al. Di-vided Segment Maximum Likelihood Registration forMultiple Moving Platforms Multiple Dissimilar Sensors[C]// Shanghai China, Proceedings of 2006 CIE Inter-national Conference on Radar, Vol. (II) : 1622 -1626.
  • 10Siyue Chen, Henry Leung, eloi Bosse. A MaximumLikelihood Approach to Joint Registration, associationand Fusion for Multi-Sensor Muhi-Target Tracking[C]//12th International Conference on Information Fu-sion Seattle. WA, USA. 2009:686-693.

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