NLOS环境中的拉格朗日乘子改进约束最小二乘定位算法

无线通信中的非视距误差是影响传统定位算法精度的主要因素.因此本文针对存在锚节点与移动节点的无线传感网络,提出了一种利用拉格朗日乘子法改进的约束最小二乘定位算法.算法核心思路在于运用拉格朗日乘子法修正约束最小二乘代价函数来构建新的目标函数,同时也提出一种分组定位组合的思想以进一步提高定位性能.仿真结果表明在非视距误差较大或网络中固定节点较多时,提出的算法可以有效消除非视距误差引起的定位精度损失,同时本文算法还具有随节点数目增加而提升优势的特性.

A NLOS Mitigation and Localization Algorithm Exploiting the Lagrange multiplier and Optimization

The non-line-of-sight (NLOS) error in wireless communication significantly reduces the accuracy of location algorithm. Therefore, by considering the wireless sensor network including both the static node (SN) and the mobile node (MN), this paper proposes a localization algorithm aiming at the Constraint?Least?Square(CLS) and Lagrange multiplier, where the NLOS corrupted localization is modeled as a optimization problem. Moreover, the location residual is employed to construct the cost function of optimization algorithm. The simulations demonstrate that the proposed algorithm can effectively reduce the positioning accuracy loss for scenarios with large NLOS errors or SN numbers. Furthermore, the proposed method works better with the increasing of the number of SNs.