Wang Dandan, Wan Jiangwen, Wang Meimei, Zhang Qiang
School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Xueyuan Road No.37, Haidian District, Beijing 100191, China.
Sensors (Basel). 2016 Jul 7;16(7):1041. doi: 10.3390/s16071041.
Precise localization has attracted considerable interest in Wireless Sensor Networks (WSNs) localization systems. Due to the internal or external disturbance, the existence of the outliers, including both the distance outliers and the anchor outliers, severely decreases the localization accuracy. In order to eliminate both kinds of outliers simultaneously, an outlier detection method is proposed based on the maximum entropy principle and fuzzy set theory. Since not all the outliers can be detected in the detection process, the Maximum Entropy Function (MEF) method is utilized to tolerate the errors and calculate the optimal estimated locations of unknown nodes. Simulation results demonstrate that the proposed localization method remains stable while the outliers vary. Moreover, the localization accuracy is highly improved by wisely rejecting outliers.
精确的定位在无线传感器网络（WSN）定位系统中引起了相当大的关注。由于内部或外部干扰，包括距离异常值和锚点异常值在内的异常值的存在严重降低了定位精度。为了同时消除这两种异常值，提出了一种基于最大熵原理和模糊集理论的异常值检测方法。由于在检测过程中并非所有异常值都能被检测到，因此利用最大熵函数（MEF）方法来容忍误差并计算未知节点的最优估计位置。仿真结果表明，所提出的定位方法在异常值变化时保持稳定。此外，通过明智地剔除异常值，定位精度得到了极大提高。
