A Semidefinite Relaxation Method for Source Localization Using TDOA and FDOA Measurements
A Semidefinite Relaxation Method for Source Localization Using TDOA and FDOA Measurements
复制标题
使用 TDOA 和 FDOA 测量进行源定位的半定弛豫方法
DOI：
10.1109/tvt.2012.2225074
复制
发表时间：
2013-02-01
影响因子：
6.8
通讯作者：
Ansari, Nirwan
中科院分区：
文献类型：
--
作者：
Wang, Gang;Li, Youming;Ansari, Nirwan
传感器网络的定位已被广泛研究。在本文中，我们通过使用到达时间差（TDOA）和到达频率差（FDOA）测量来解决源定位问题。由于最大似然（ML）估计问题的非凸性质，如果没有良好的初始估计，很难获得其全局最优解。因此，我们将定位问题重新表述为加权最小二乘（WLS）问题，并执行半定松弛（SDR）以获得凸半定规划（SDP）问题。尽管SDP是原始WLS问题的松弛，但它有助于准确估计而无需后处理。此外，该方法还被扩展以解决传感器位置和速度存在误差时的定位问题。仿真结果表明，所提出的方法比现有方法取得了显着的性能提升。
Localization by a sensor network has been extensively studied. In this paper, we address the source localization problem by using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. Owing to the nonconvex nature of the maximum-likelihood (ML) estimation problem, it is difficult to obtain its globally optimal solution without a good initial estimate. Thus, we reformulate the localization problem as a weighted least squares (WLS) problem and perform semidefinite relaxation (SDR) to obtain a convex semidefinite programming (SDP) problem. Although SDP is a relaxation of the original WLS problem, it facilitates accurate estimate without postprocessing. Moreover, this method is extended to solve the localization problem when there are errors in sensor positions and velocities. Simulation results show that the proposed method achieves a significant performance improvement over existing methods.