Unified Orthogonal Cubature Kalman Tracking Algorithm Based on TOA/TDOA

YAN Lei-bing; LU Yin; ZHANG Ye-rong

doi:10.3969/j.issn.0372-2112.2018.08.026

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Unified Orthogonal Cubature Kalman Tracking Algorithm Based on TOA/TDOA

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- Unified Orthogonal Cubature Kalman Tracking Algorithm Based on TOA/TDOA
- Acta Electronica Sinica Vol. 46, Issue 8, Pages: 1989-1996(2018)
- 作者机构：
  
  1. 河南工学院电子通信工程系,河南,新乡,453003
  2. 南京邮电大学电子科学与工程学院,江苏,南京,210003
  3. 河南工学院电子通信工程系,河南,新乡,453003
  4. 南京邮电大学电子科学与工程学院,江苏,南京,210003
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61271236, No.61601245);Open Research Subject of State Key Laboratory of Millimeter Waves, Southeast University (No.K201724);Post-doctoral Foundation of China (No.2016M601693);Research Project of Nanjing University of Posts and Telecommunications (No.NY213081)
- DOI：10.3969/j.issn.0372-2112.2018.08.026
  CLC： TN953
- Published Online：25 August 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
YAN Lei-bing, LU Yin, ZHANG Ye-rong. Unified Orthogonal Cubature Kalman Tracking Algorithm Based on TOA/TDOA[J]. Acta Electronica Sinica, 2018, 46(8): 1989-1996.
DOI：

YAN Lei-bing, LU Yin, ZHANG Ye-rong. Unified Orthogonal Cubature Kalman Tracking Algorithm Based on TOA/TDOA[J]. Acta Electronica Sinica, 2018, 46(8): 1989-1996. DOI： 10.3969/j.issn.0372-2112.2018.08.026.

摘要

针对异构网络环境下目标跟踪系统中面临的非线性估计问题，提出了一种基于正交容积卡尔曼滤波的目标跟踪算法.文中算法首先引入一个附加变量来表示状态变量中的非线性项，设定自适应加权因子来调整不同信号输入系统的比重，然后利用数学手段融合两种信号为单一的状态变量，最后通过正交容积卡尔曼滤波来实现目标状态量的更新，从而实现对目标的定位与跟踪.仿真结果表明了文中算法的有效性，能够得到更高的定位与跟踪精度.

Abstract

In view of the nonlinear estimation problem of target tracking system under heterogeneous network environment

a unified orthogonal cubature Kalman target tracking(UOCKT)approach is proposed.The new approach introduce an additional variable to represent the nonlinear terms of the state variables

the adaptive weighting factor is set to adjust the specific gravity of the different signal input system

and then the two kinds of signals are merged into a single state variable by mathematical means

and finally through the orthogonal cubature Kalman filter(OCKF) to achieve the target state quantity update

so as to realize the positioning and tracking of the target.The simulation results show that the algorithm is effective and can get higher positioning and tracking accuracy.

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