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低可观测条件下传感器空间配准方法 被引量:2

Spatial Registration Method for Sensors in Low Observability Environment
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摘要 在地心地固(ECEF)坐标系下,推导了传感器测量系统误差的状态方程和测量方程;从线性时变系统能观性理论引出了系统误差可观测性定义,并给出了可观测性的度量方法;讨论了两种典型的低可观测情况,并根据各自特点提出了相应的解决方法。最后,用一个案例仿真证明了相关结论。 State equations and measurement equations for system errors of the sensors measure- ment are deduced in the earth-centered earth-fixed (ECEF) coordinate system and the observabil- ity definition for system errors is given by using the observability theory of the linear time-vary- ing control system. In addition, the measurement method for the observability is obtained. Then, two typical situations with low observability are discussed and algorithms for the system errors are proposed. Finally, a simulation experiment proves the conclusion.
出处 《指挥信息系统与技术》 2012年第1期54-58,共5页 Command Information System and Technology
关键词 空间配准 系统误差 可观测性 病态矩阵 spatial registration system error observability illness matrix
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  • 1杨宏文,郁文贤,胡卫东,吴建辉.基于数据补偿的雷达系统误差估计[J].火力与指挥控制,2000,25(2):23-27. 被引量:6
  • 2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[J].系统仿真学报,2005,17(7):1583-1586. 被引量:6
  • 3王波,王灿林,董云龙.RTQC误差配准算法性能分析[J].系统仿真学报,2006,18(11):3067-3069. 被引量:15
  • 4Golub H G. Some modified matrix eigenvalue problems[ J]. SIAM Rev. , 1973, 15:318-344.
  • 5Golub H G and Van Loan F C. An analysis of the total least squares problem[J]. SIAM Journal on Numerical Analysis, 1980, 17(6) :883 -893.
  • 6Markovsky I, et al. The element-wise weighted total leastsquares problem [ C ]. Comput Statist Data, 2006, Anal50 (1) :181 -209.
  • 7Lemmerling P. Structured total least squares : Analysis, algorithms and applications [ D ]. Katholieke Universiteit, Leuyen, Belgium, 1999.
  • 8Schaffrin B and Felus Y A. On total least-squares adjustment with constraints [ J ]. A windows on the future of Geodesy,IAG - Symp, 2005, Springer, Berlin, t28:417 - 421.
  • 9Sehaffrin B. A note on constrained total least-squares estimation [J]. Linear Algebra Application , 2006, 417( 1 ) :245 - 258.
  • 10Felus Y A and Schaffrin B. Performing similarity transformations using the error-in-variables model[ A]. American society for photogrammetry and remote sensing(ASPRS) annual meeting[ C ]. Baltimore,Maryland, 2003 on CD.

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