Li Wei, Yang Hai, Fan Mengbao, Luo Chengming, Zhang Jinyao, Si Zhuoyin
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.
College of Internet of Things Engineering, Hohai University, Changzhou 213022, Jiangsu, China.
Micromachines (Basel). 2016 Nov 2;7(11):197. doi: 10.3390/mi7110197.
In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.
近年来，封闭环境中的移动目标定位受到越来越多的关注。在本文中，我们提出了一种模糊自适应紧密耦合集成（FATCI）方法，用于使用捷联惯性导航系统（SINS）和无线传感器网络（WSN）的定位和跟踪应用。WSN的无线信号中断和严重的多径传播经常影响测量距离的准确性，并导致WSN定位困难。还应注意，SINS以其随时间漂移的误差而闻名。我们以著名的松散耦合集成方法为基础，基于锚节点与移动节点之间的测量距离，构建了一种用于SINS/WSN的紧密耦合集成定位系统。使用最小二乘回归（LSR）算法对WSN的测量距离值进行校正，以减少测量距离的系统误差。此外，基于统计特性，使用模糊推理系统（FIS）将测量距离值的统计协方差用于调整卡尔曼滤波器的观测协方差矩阵。然后，紧密耦合集成模型可以根据距离测量的不同测量精度自适应地调整测量的置信水平。因此，利用SINS/WSN实现了FATCI系统。这种创新方法在实际场景中得到了验证。实验结果表明，与用于WSN短期故障或正常情况的松散耦合和传统紧密耦合集成模型相比，所提出的定位系统具有更好的准确性和稳定性。
