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

作　　者：张爱琳 刘辉 王小海 张秀伊 邱正中 吴春国[1,2] ZHANG Aili;LIU Hui;WANG Xiaohai;ZHANG Xiuyi;QIU Zhengzhong;WU Chungu(Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education;College of Computer Science and Technology,Jilin University,Changchun 130012,China;Technical Department,Yuanguang Software Company Limited INC,Zhuhai 519085,China)

机构地区：[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 [2]吉林大学计算机科学与技术学院,长春130012 [3]远光软件股份有限公司技术部,广东珠海519085

出　　处：《吉林大学学报（信息科学版）》2024年第2期193-199,共7页Journal of Jilin University（Information Science Edition）

基　　金：吉林省科技发展计划基金资助项目(20230201083GX)。

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。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 

分 类 号：TN925[电子电信—通信与信息系统]