摘要
为解决井下人员定位算法定位精度不高的问题,提出基于微惯性导航系统和无线传感器网络的井下组合导航定位算法。通过井下无线网络、惯性定位终端采集相关信息数据,利用行人航迹推算算法和改进加权质心定位算法分别估算出目标点的坐标和速度。将这两种算法通过正弦余弦蝙蝠融合算法优化后的卡尔曼滤波组合导航定位,估算出目标点最终的位置坐标。实验结果表明:该组合导航定位算法平均定位误差为1.71 m,其定位精度高于传统质心定位算法和行人航迹推算算法。
In order to solve the problem of low positioning accuracy of underground personnel positioning algorithm,an underground integrated navigation positioning algorithm based on micro-inertial-navigation system and wireless sensor networks is proposed.We collected relevant information data through underground wireless network and inertial positioning terminal,and estimated the coordinates and speed of target points respectively by using pedestrian track estimation algorithm and improved weighted centroid positioning algorithm.The two algorithms were located by Kalman filter integrated navigation optimized by Sine cosine bat fusion algorithm,and the final position coordinates of the target point were estimated.The experimental results show that the average positioning error of the integrated navigation positioning algorithm is 1.71 m,and its positioning accuracy is higher than that of the traditional centroid positioning algorithm and pedestrian trajectory estimation algorithm.
作者
芦宝娟
赵大磊
徐广允
Lu Baojuan;Zhao Dalei;Xu Guangyun(School of Electronics and Information Engineering,Guizhou Industry Polytechnic College,Guiyang 551400,Guizhou,China)
出处
《计算机应用与软件》
北大核心
2022年第3期139-145,共7页
Computer Applications and Software
基金
贵州省科技厅资助项目(黔科合LH字[2016]7069号)。
关键词
微惯性导航系统
无线传感器网络
行人航迹推算
加权质心定位
正弦余弦算法
蝙蝠算法
卡尔曼滤波
井下组合导航定位算法
Micro-inertial-navigation system
Wireless sensor networks
Pedestrian trajectory estimation
Weighted centroid positioning
Sine cosine algorithm
Bat algorithm
Kalman filtering
Underground integrated navigation positioning algorithm
