信号干扰下的超宽带精确定位问题研究
Research on Precise Positioning of Ultra Wide Band with Signal Interference
张爱琳 1刘辉 2王小海 2张秀伊 1邱正中 1吴春国1
作者信息
- 1. 吉林大学符号计算与知识工程教育部重点实验室,长春 130012;吉林大学 计算机科学与技术学院,长春 130012
- 2. 远光软件股份有限公司技术部,广东珠海 519085
- 折叠
摘要
针对在室内应用超宽带UWB(Ultra Wide Band)定位技术时,需要建立高效精确的三维坐标定位系统以克服信号干扰问题,应用机器学习方法对其进行了研究.首先使用多种统计分析模型清理无效或误差测量值;然后将TOF(Time Of Flight)算法的先验知识与神经网络、XGBoost(eXterme Gradient Boosting)算法相结合,提出了神经XGB(Exterme Gradient Boosting)三维定位系统,该系统可通过"正常数据"和"异常数据"(受干扰)以及4个锚点的坐标精准预测靶点的坐标值,能使误差在二维平面降至5.08 cm,在三维空间降至8.03 cm;同时建立了判断数据是否受干扰的神经网络分类模型,精确率为0.88;最后通过结合上述系统,得到了连续且规律的运动轨迹,证明了系统的有效性与鲁棒性.
Abstract
In the field of indoor applications of UWB(Ultra Wide Band)positioning technology,it is important to establish an efficient and accurate 3D coordinate positioning system to overcome signal interference.Machine learning methods are used to investigate the problem of accurate positioning of indoor UWB signals under interference.Firstly,various statistical analysis models are used to clean up invalid or error measurements,then the a priori knowledge of TOF(Time Of Flight)algorithm is combined with neural network and XGBoost algorithm to build a neural XGB(Exterme Gradient Boosting)3D oriented system.The system can accurately predict the coordinate value of the target point by"normal data"and"abnormal data"(disturbed),the coordinates of four anchor points,and the final error is as low as 5.08 cm in two-dimensional plane and 8.03 cm in three-dimensional space.A neural network classification system is established to determine whether the data is disturbed or not,with an accuracy of 0.88.Finally,by combining the above systems,continuous and regular motion trajectories are obtained,which proves the effectiveness and robustness of the systems.
关键词
UWB精准定位/神经网络/XGBoost算法/逻辑回归Key words
ultra wide band(UWB)precision positioning/neural network/XGBoost algorithm/logistic regression引用本文复制引用
基金项目
吉林省科技发展计划(20230201083GX)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据