J4 ›› 2010, Vol. 37 ›› Issue (3): 447-453.doi: 10.3969/j.issn.1001-2400.2010.03.011

• 研究论文 • 上一篇    下一篇

基于最小化测量误差的被动定位算法

陈金广1,2;李洁1;高新波1
  

  1. (1. 西安电子科技大学 电子工程学院,陕西 西安  710071;
    2. 西安工程大学 计算机科学学院,陕西 西安  710048)
  • 收稿日期:2009-09-02 出版日期:2010-06-20 发布日期:2010-07-23
  • 通讯作者: 陈金广
  • 作者简介:陈金广(1977-),男,西安电子科技大学博士研究生,E-mail: xacjg@163.com.
  • 基金资助:

    国家自然科学基金资助项目(60832005,60702061);教育部长江学者和创新团队支持计划资助项目(IRT0645)

Passive localization algorithm for minimizing the measurement error

CHEN Jin-guang1,2;LI Jie1;GAO Xin-bo1   

  1. (1. School of Electronic Engineering, Xidian Univ., Xi'an  710071, China;
    2. School of Computer Science, Xi'an Polytechnic Univ., Xi'an  710048, China)
  • Received:2009-09-02 Online:2010-06-20 Published:2010-07-23
  • Contact: CHEN Jin-guang

摘要:

借助传感器各自的坐标位置及其三角函数关系,推导出了各个传感器之间测量值的约束关系式.然后将传感器测量误差作为变量构造目标函数,通过拉格朗日-牛顿最优化方法最小化测量误差.使用这些误差修正测量值,取得了较好的目标定位结果.该算法不需要设置目标位置初值,定位结果不会发散,并且具有较高的定位精度.仿真实验结果验证了该算法的有效性.

关键词: 数据融合, 目标定位, 被动传感器, 约束最优化, 拉格朗日-牛顿最优化方法

Abstract:

In the passive localization of multiple passive sensors, there are some constraints among sensors'position, measurement error and measurements. Measurement errors will be reduced if the constraint can be used rationally. The paper derives constraint formulas for measurements of different sensors by means of every sensor's coordinate and corresponding trigonometric function. Then objective functions which regard measurement errors as variables are given. The measurement errors are minimized by the Lagrange-Newton optimization method, and then measurements can be modified. Finally, more accurate results of target localization can be obtained. The proposed algorithm does not need the initial target position and the results did not diverge, and the proposed algorithm has a better localization accuracy. Simulation results verify the effectiveness of the new algorithm.

Key words: data fusion, target localization, passive sensors, constrained optimization, Lagrange-Newton optimization method