航空学报 > 2015, Vol. 36 Issue (5): 1617-1626   doi: 10.7527/S1000-6893.2015.0010

基于定位误差修正的运动目标TDOA/FDOA无源定位方法

刘洋1, 杨乐1,2, 郭福成1, 姜文利1   

  1. 1. 国防科学技术大学 电子科学与工程学院, 长沙 410073;
    2. 江南大学 物联网工程学院, 无锡 214122
  • 收稿日期:2014-06-15 修回日期:2015-01-07 出版日期:2015-05-15 发布日期:2015-01-23
  • 通讯作者: 郭福成Tel.: 0731-84573490 E-mail: gfcly@21cn.com E-mail:gfcly@21cn.com
  • 作者简介:刘洋 男, 博士研究生。主要研究方向:无源定位和雷达信号处理。Tel: 0731-84573490 E-mail: ruben052013@126.com;杨乐 男, 博士, 副教授, 硕士生导师。 主要研究方向: 无源定位和传感器网络、目标跟踪及信号检测。Tel: 0731-84573490 E-mail: le.yang.le@gmail.com;郭福成 男, 博士, 教授, 博士生导师。 主要研究方向: 无源定位、跟踪滤波、信号处理技术。Tel: 0731-84573490 E-mail: gfcly@21cn.com;姜文利 男, 博士, 教授, 博士生导师。主要研究方向: 综合电子战技术、空间信息处理。Tel: 0731-84573490 E-mail: jiangwenlibetter@163.com
  • 基金资助:

    国家自然科学基金(61304264);国防科技重点实验室基金 (9140C860304)

Moving targets TDOA/FDOA passive localization algorithm based on localization error refinement

LIU Yang1, YANG Le1,2, GUO Fucheng1, JIANG Wenli1   

  1. 1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;
    2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
  • Received:2014-06-15 Revised:2015-01-07 Online:2015-05-15 Published:2015-01-23
  • Supported by:

    National Natural Science Foundation of China(61304264); Foundation of National Defense Key Laboratory of China (9140C860304)

摘要:

针对时差(TDOA)、频差(FDOA)无源定位的两步加权最小二乘(TSWLS)方法定位均方根误差(RMSE)和定位偏差适应测量噪声能力差的问题,在分析了影响两步法定位性能的因素基础上提出一种基于一阶泰勒级数展开的定位误差修正方法。该方法的第1步和两步法相同;其第2步避免了两步法第2步中引入估计偏差的平方运算,利用一阶泰勒级数展开得到第1步定位误差的线性最小均方估计,修正第1步定位结果得到目标位置和速度的最终估计,从理论上证明了该方法可以达到定位的克拉美罗下限(CRLB)。计算机仿真对比了新方法和TSWLS方法、基于泰勒级数(TS)展开的迭代极大似然(ML)方法以及约束总体最小二乘(CTLS)方法的定位性能,新算法复杂度和两步法相当,且均方误差和定位偏差低于两步法、泰勒级数法和CTLS方法。

关键词: 时差, 频差, 定位, 泰勒级数, 加权最小二乘

Abstract:

For the two-stage weighted least squares (TSWLS) technique of passive source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which has the problem that the root mean square error (RMSE) and localization bias is large as the measurement noise increases. Based on analyzing the factor influencing the performances of the TSWLS firstly and then improves the TSWLS via Taylor-series (TS) expansion technique. The first stage of the new algorithm is the same as the one of TSWLS. At the second stage of the new algorithm, the localization error of the first stage is identified through utilizing the first-order Taylor-series expansion. Through updating the first-stage localization error, the final localization output is obtained. Theoretical performance analysis shows that the proposed estimator can attain the Cramer-Rao lower bound (CRLB) accuracy. Computer simulations are used to contrast the new technique with the TSWLS algorithm, the iterative maximum likelihood method based on TS and the constrained total least squares (CTLS) algorithm in terms of their localization RMSE and the localization bias. The new algorithm whose complexity is almost the same as TSWLS, the RMSE and localization bias are lower than TSWLS, TS and CTLS algorithm.

Key words: time difference of arrival, frequency difference of arrival, localization, Taylor-series, weighted least squares

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