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Semidefinite Programming Two-Way TOA Localization for User Devices With Motion and Clock Drift
IEEE Signal Processing Letters ( IF 3.2 ) Pub Date : 2021-03-11 , DOI: 10.1109/lsp.2021.3064755
Sihao Zhao , Xiao-Ping Zhang , Xiaowei Cui , Mingquan Lu

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position by round-trip communications to a number of anchor nodes (ANs) at known locations. The objective function of the maximum likelihood (ML) method for two-way TOA localization is nonconvex. Thus, the widely-adopted Gauss-Newton iterative method to solve the ML estimator usually suffers from the local minima problem. In this letter, we convert the original estimator into a convex problem by relaxation, and develop a new semidefinite programming (SDP) based localization method for moving UDs, namely SDP-M. Numerical result demonstrates that compared with the iterative method, which often fall into local minima, the SDP-M always converge to the global optimal solution and significantly reduces the localization error by more than 40%. It also has stable localization accuracy regardless of the UD movement, and outperforms the conventional method for stationary UDs, which has larger error with growing UD velocity.

中文翻译:

具有运动和时钟漂移的用户设备的半定编程双向TOA本地化

在双向到达时间(TOA)系统中,用户设备(UD)通过与已知位置的多个锚点(AN)的往返通信获得其位置。用于双向TOA定位的最大似然(ML)方法的目标函数是非凸的。因此,广泛采用的高斯-牛顿迭代法来求解ML估计量通常会遇到局部极小值问题。在这封信中,我们通过松弛将原始估计量转化为凸问题,并开发了一种新的基于半定规划(SDP)的移动UD定位方法,即SDP-M。数值结果表明,与经常落入局部极小值的迭代方法相比,SDP-M始终收敛于全局最优解,可将定位误差显着降低40%以上。
更新日期:2021-04-02
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