Target tracking by time difference of arrival using recursive smoothing
Target tracking by time difference of arrival using recursive smoothing
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DOI：
10.1016/j.sigpro.2004.11.007
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发表时间：
2005-04-01
影响因子：
4.4
通讯作者：
Hashemi-Sakhtsari, A
中科院分区：
文献类型：
--
作者：
Dogançay, K;Hashemi-Sakhtsari, A
针对机动目标的被动跟踪问题，提出了一种简单的递归方法。首先，提出了一种基于约束加权最小二乘准则的迭代高斯-牛顿静止目标定位算法；CWLS估计的优点是由于在无穷远处不存在局部最小值而具有固有的稳定性，并且能够匹配最大似然估计的性能。为了跟踪机动目标，提出了一种计算效率高的递推最小二乘（RLS）算法，该算法利用恒加速度运动模型对ML或CWLS解获得的连续静止目标位置估计进行平滑处理。在仿真研究中，将所提出的递归跟踪算法与直接根据TDOA测量估计目标轨迹的卡尔曼跟踪算法进行了比较，结果表明该算法的性能优于卡尔曼跟踪算法。(c) 2004 Elsevier B.V.版权所有
The paper presents a simple recursive solution to passive tracking of maneuvering targets using time difference of arrival (TDOA) measurements. Firstly, an iterative Gauss-Newton algorithm is developed for stationary target localization based on a constrained weighted least-squares (CWLS) criterion. The advantages of the CWLS estimate are its inherent stability due to the absence of local minima at infinity and its capability to match the performance of the maximum-likelihood (ML) estimate. To track maneuvering targets, a computationally efficient recursive least-squares (RLS) algorithm is developed, which smoothes successive stationary target location estimates obtained from the ML or CWLS solution using a con stant-acceleration motion model. In simulation studies, the proposed recursive tracking algorithm is compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, and is shown to be capable of outperforming the Kalman tracker. (c) 2004 Elsevier B.V. All rights reserved.