摘要
为了降低到达时间差(TDOA)测距在非视距(NLOS)传播环境中的误差,提出了在强跟踪无迹卡尔曼滤波(UKF)基础上改进的算法。在状态发生突变时,给预测协方差矩阵加入次优渐消因子;对NLOS误差进行正负判断,利用整体偏移法修改滤波增益,但估计协方差矩阵不做改进,以免出现不收敛。实验结果表明:该算法不仅能有效地抑制突变带来的影响,也能高效地消除NLOS误差,提高了NLOS传播的到达时间差定位精度。
In order to reduce time difference of arrival（ TDOA） ranging error in non line of sight（ NLOS）environments,two improvements are introduced to enhance unscented Kalman filtering（ UKF）. When the system states change,suboptimal fading factors are added into the prediction covariance to eliminate the influence of mutational states. Secondly,judging type of NLOS error and using the total-deflection method can modify filtering gain,but estimation covariance matrix is not modified to avoid non-convergence. Experimental result indicates that the proposed method can not only restrain the influence of mutation,but also eliminate NLOS error highly effectively and improve positioning precision greatly.
作者
刘恋
向凤红
毛剑琳
LIU Lian XIANG Feng-hong MAO Jian-lin(Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, Chin)
出处
《传感器与微系统》
CSCD
2017年第4期138-142,共5页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61163051
F020809)
关键词
到达时间差
无线传感器网络
非视距传播
无迹卡尔曼滤波
滤波增益
time difference of arrival（TDOA）
wireless sensor networks（WSNs）
non line of sight（NLOS） propagation
unscented Kalman filtering（UKF）
filtering gain
