An Iterative Method for Moving Target Localization Using TDOA and FDOA Measurements
An Iterative Method for Moving Target Localization Using TDOA and FDOA Measurements
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DOI：
10.1109/access.2017.2785182
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
Yanbin Zou;Huaping Liu;Q. Wan
中科院分区：
文献类型：
--
作者：
Yanbin Zou;Huaping Liu;Q. Wan
对于运动目标的定位，在常用的时差定位系统中加入频差（FDOA）测量可以提高定位性能。这种方法仍然存在尚未解决的技术挑战。常用的最大似然估计器（MLE）是非凸的、高度非线性的，并且在定位过程中待估计的参数是相互耦合的。本文的目标是开发一种有效的迭代方法，解决这些挑战的运动目标定位使用时差和频差。具体地说，半定规划（SDP）方法提出的MLE问题转化为凸优化问题。为了进一步提高性能，我们开发了一种迭代方法，使用的位置和速度估计使用SDP方法作为初始值。该迭代方法包括两个步骤：使用加权最小二乘法更新速度和使用SDP更新位置。该方案的主要优点是，在中高噪声水平下，它显着优于现有方法，这一点通过大量数值结果得到了验证。
For moving targets localization, incorporating frequency-difference-of-arrival (FDOA) measurements in the commonly used time-difference-of-arrival (TDOA) positioning systems will improve performance. Such an approach still has unresolved technical challenges. The commonly used maximum likelihood estimator (MLE) is nonconvex and highly nonlinear, and the parameters to be estimated are mutually coupled in the positioning process. The goal of this paper is to develop an effective iterative method that resolves these challenges for moving target localization using TDOA and FDOA. Specifically, a semidefinite programming (SDP) method is proposed to transform the MLE problem into a convex optimization problem. To improve the performance further, we develop an iterative method that uses the position and velocity estimates obtained using the SDP method as the initial values. This iterative method includes two steps: update of the velocity by using a weighted least squares method and update of the position by using SDP. The major advantage of the proposed scheme is that it significantly outperforms existing methods at moderate to high noise levels, which is validated via extensive numerical results.