期刊文献+

运动多平台加权辅助变量时差连续定位算法

Weighted Instrumental Variable Algorithm for Moving Multi-Platform TDOA-Based Continuous Localization
下载PDF
导出
摘要 针对已有时差(TDOA)定位模型通常需要引入一个恒定中间变量而不适合运动多平台连续定位的不足,推导了三维空间中无需中间变量的TDOA定位模型并在此基础上提出了一种加权辅助变量(WIV)连续定位算法。该算法首先推导代入TDOA测量值后新定位方程的误差项,求得误差项的协方差后将其用于构造最优辅助变量(IV)矩阵,并采用总体最小二乘(TLS)算法的估计值计算次优IV矩阵。仿真结果表明,所提WIV算法能够有效实现运动多平台TDOA连续定位。 Existing time-difference-of-arrival (TDOA) localization model usually needs to introduce an intermediate variable and thus is not suitable for moving multi-platform continuous localization. To solve this problem, a TDOA model in 3-D space is deduced which doesn't need any intermediate variable and a weighted instrumental variable (WIV) is proposed based on it. The proposed algorithm first deduces the error item of the new localization equation after substituting the true TDOA for its measured counterpart. The covariance of the error item is deduced to construct an optimal instrumental variable (IV) matrix and the total least squares (TLS) algorithm is used for sub-optimal IV matrix calculation. Simulation results show the effectiveness of the proposed WIV algorithm for moving multi-platform TDOA- based continuous localization.
作者 骆卉子 曲长文 徐征 LUO Hui-zi QU Chang-wen XU Zheng(Naval Aeronautical and Astronautical University,Electronic and Information Engineering Department Shandong Yantai 264001, China)
出处 《现代防御技术》 北大核心 2017年第1期126-131,共6页 Modern Defence Technology
基金 泰山学者建设工程专项经费资助
关键词 定位 多平台 时差 三维空间 加权辅助变量 总体最小二乘 positioning ( localization ) multi-platform time difference of arrival ( TDOA ) three dimensional ( 3-D ) space weighted instrumental variable ( WIV ) total least squares (TLS)
  • 相关文献

参考文献3

二级参考文献37

  • 1许为武.引人注目的维拉无源雷达系统[J].国际航空,2004(7):10-11. 被引量:2
  • 2陈永光,孙仲康.T/R-R型双基地系统分段跟踪技术的研究[J].航空学报,1994,15(12):1515-1519. 被引量:7
  • 3苏卫民,顾红,张先义.基于外辐射源的雷达目标探测与跟踪技术研究[J].现代雷达,2005,27(4):19-22. 被引量:14
  • 4俞志强,王宏远,武文.四站时差定位布站研究[J].电子学报,2005,33(B12):2308-2311. 被引量:17
  • 5KIM B D,LEE J S.IMM Algorithm Based on the Ana- lytic Solution of Steady State Kalman Filter for Radar Target Tracking[C]//Radar Conference,2005 IEEE International.IEEE,2005:757-762.
  • 6MAZOR E,AVERBUCH A,BAR-SHALOM Y,et al. Interacting Multiple Model Methods in Target Tracking: a Survey[J].Aerospace and Electronic Systems,IEEE Transactions on,1998,34(1):103-123.
  • 7SUNAHARA Y,YAMASHITA K.An Approximate Method of State Estimation for Non-Linear Dynamical Systems with State-Dependent Noise[J].International Journal of Control,1970,11(6):957-972.
  • 8JULIER S J,UHLMANN J K.Unscented Filtering and Nonlinear Estimation[J].Proceedings of the IEEE,2004,92(3):401-422.
  • 9WAN E A,Yan Der Merwe R.The Unscented Kalman Filter for Nonlinear Estimation[C]//Adaptive Systems for Signal Processing,Communications,and Control Symposium 2000.AS-SPCC.The IEEE 2000.IEEE,2000;153-158.
  • 10ARASARATNAM I,HAYKIN S.Cubature Kalman Fil- ters[J].Automatic Control,IEEE Transactions on,2009,54(6):1254-1269.

共引文献39

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部