基于定位误差修正的运动目标TDOA/FDOA无源定位方法
收稿日期: 2014-06-15
修回日期: 2015-01-07
网络出版日期: 2015-01-23
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
Received date: 2014-06-15
Revised date: 2015-01-07
Online published: 2015-01-23
Supported by
National Natural Science Foundation of China(61304264); Foundation of National Defense Key Laboratory of China (9140C860304)
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。
刘洋 , 杨乐 , 郭福成 , 姜文利 . 基于定位误差修正的运动目标TDOA/FDOA无源定位方法[J]. 航空学报, 2015 , 36(5) : 1617 -1626 . DOI: 10.7527/S1000-6893.2015.0010
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.
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