Fang Xuming, Chen Lijun
School of Software Engineering, Jinling Institute of Technology, Nanjing 210000, China.
Department of Computer Science and Technology, Nanjing University, Nanjing 210000, China.
Sensors (Basel). 2020 Mar 24;20(6):1798. doi: 10.3390/s20061798.
The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.
全球定位系统（GPS）无法在室内提供精确的定位服务，这使得无线传感器网络（WSN）定位技术成为室内定位领域的一个热门研究问题。目前，基于接收信号强度的无线传感器网络测距技术已广泛应用于室内定位。然而，无线信号在室内环境中存在严重的多径效应。为了减少多径效应对节点间距离估计的不利影响，本文提出了一种基于信号分集的多通道测距定位算法。在实际室内环境中，用于多通道定位算法的参数通常是未知的或时变的。为了提高多通道定位算法在多径环境下的定位精度，我们通过建立一种新颖的多目标进化模型，提出了一种最优多通道三边测量定位算法（OMCT）。该算法利用三边约束防止传统多通道定位算法陷入局部最优。大量实际实验和数值模拟结果表明，无论通道数和多径数如何变化，我们提出的算法的定位误差始终比现有算法小。
