一种融合卡尔曼滤波的室内组合定位算法  

Combined Indoor Positioning Algorithm Based on Kalman Filter

作　　者：徐淑萍[1] 熊小墩 郭宇 苏小会[1] 张玉西 XU Shuping;XIONG Xiaodun;GUO Yu;SU Xiaohui;ZHANG Yuxi(School of Computer Science and Engineering,Xi’an Technological University,Xi’an 710021,China)

机构地区：[1]西安工业大学计算机科学与工程学院,西安710021

出　　处：《西安工业大学学报》2022年第3期299-305,338,共8页Journal of Xi’an Technological University

摘　　要：为了提高室内移动机器人的定位精度,提出一种抑制定位误差的Chan Taylor Kalman组合定位算法。该算法在Chan Taylor协同定位算法基础上,引入权重因子,再融合卡尔曼滤波算法优化定位观测数据,从而得到最终的目标估计值。实验结果表明:相比Chan Taylor协同定位算法,Chan Taylor Kalman组合定位算法的定位均方根误差静态环境下在7.54 cm,下降了约13.2%,动态环境下则集中在9 cm,定位性能更稳定,能满足室内移动机器人定位精度的要求。In order to improve the positioning accuracy of indoor mobile robots,a Chan Taylor Kalman combined positioning algorithm is proposed to suppress the positioning error.With weight factor introduced,the Chan Taylor co location algorithm is integrated with the Kalman filter algorithm to optimize the positioning observation data for the final target estimation value.The experimental results show that compared with the Chan Taylor co localization algorithm,the Chan Taylor Kalman combined positioning algorithm has the root mean square error of 7.54 cm in the static environment,decreasing by about 13.2%,and of 9 cm in the dynamic environment.It is concluded that this algorithm has a more stable positioning performance and can meet the requirements of positioning accuracy of indoor mobile robots.

关 键 词：室内定位 Chan Taylor协同算法 权重因子 卡尔曼滤波 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]