摘要
针对室内接收信号强度指示(RSSI)测距定位中,测距精度较低、易受环境影响的问题,提出了一种基于对数鲁棒函数(LnRLS)的UKF改进算法,有效改善测距误差,提高定位精度。在室内环境下,首先采用卡尔曼滤波对接收到的RSSI值进行预处理,运用最小二乘法拟合传播方程,使用改进的UKF算法对数据进行二次处理得出测量距离,通过三边定位算法估计节点坐标。将实验结果与其他文献采用的传统UKF测距算法及室内定位方法相比,改进算法将测距误差降低了14.4%,有效的减少测距误差,提高室内定位系统的定位精度。
For the indoor Received Signal Strength Indication（RSSI） location, location accuracy is low, and can be easily affected by environment, the work proposes an improved Unscented Kalman Filter（UKF） algorithm based on logarithmic robust function （LnRLS）, which can effectively improve the ranging error and improve the positioning ac- curacy. In the indoor environment, firstly, we used the Kalman Filter to preprocess the received RSSI value, we fitted equation by using the least squares, and calculated the distance by using the improved UKF algorithm for the two time, estimated node coordinates by the three edge location algorithm. Compared the experimental results with other tradi- tional UKF ranging algorithm and indoor positioning methods, the improved algorithm reduces the ranging error by 14. 4% , which can effectively reduce the ranging error and improve the positioning accuracy of indoor positioning sys- tem.
作者
邹胜男
陈晓
陈霞
ZOU Sheng-nan CHEN Xiao CHEN Xia(School of electronics and information engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China Atmospheric Environment and Equipment Technology Collaborative Innovation Center, Nanjing University of Information Science and Technology, Nanjing 210044, China)
出处
《激光杂志》
北大核心
2017年第4期61-65,共5页
Laser Journal
基金
江苏省第十一批"六大人才高峰"高层次人才项目资助
江苏省自然科学基金(BK20161536)
江苏省333高层次人才培养工程
江苏高校优势学科Ⅱ期建设工程资助项目
关键词
对数鲁棒函数
最小二乘法
无迹卡尔曼滤波
logarithmic robust function
least square method
Unscented Kalman Filter
