基于UWB的密集行人三维协同定位算法  

Three-dimensional cooperative location algorithm of dense pedestrians based on UWB

作　　者：贺军义[1] 吴梦翔 宋成[1] 张敏 张俊楠 He Junyi;Wu Mengxiang;Song Cheng;Zhang Min;Zhang Junnan(College of Computer Science&Technology,Henan Polytechnic University,Jiaozuo Henan 454002,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454002

出　　处：《计算机应用研究》2022年第3期790-796,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61872126,61772159);河南省科技攻关项目(212102210092);河南省高校重点研究基金资助项目(20A520015);博士基金资助项目(60907023)。

摘　　要：针对复杂室内环境中密集行人定位精度低、超宽带(UWB)基站密度要求高的问题,提出一种基于UWB的密集行人三维协同定位算法。首先使用聚类算法抑制测距数据中较大非视距(NLOS)误差,并使用高斯均值混合滤波抑制标准测量误差;然后提出双层协同定位算法,建立协同定位数学模型,并结合迭代初始值获取策略进行初步定位,降低了基站数量要求,在筛选出NLOS误差较小的测距数据并修正后,进行二次定位;最后考虑行人高机动性,设计一种交互多模型卡尔曼滤波算法,缓解了定位结果跳变问题。实验结果表明,所提算法在弱NLOS环境和强NLOS环境下定位精度分别达到0.11 m、0.17 m,相比其他算法,具有较高定位精度,进一步降低了对UWB基站密度要求。In view of the problem that low positioning accuracy of dense pedestrians and high density requirement of UWB base stations in complex indoor environment,this paper developed a three-dimensional cooperative location algorithm for dense pedestrians based on UWB.Firstly,it used clustering algorithm to suppress the large non-line-of-sight(NLOS)error in the ranging data,and suppressed the standard measurement error with the Gaussian mean mixture filtering.Then,it proposed a two-layer cooperative positioning algorithm,established a co-localization mathematical model and combined iterative initial value acquisition strategies for initial localization,which reduced the requirement for number of base stations.After screening the ranging data with smaller NLOS errors and correcting them,it performed secondary positioning.Finally,considering the high mobility of pedestrians,it designed an interactive multi-model Kalman filter algorithm to alleviate the problem of jumps in positioning results.Experimental results show that the positioning accuracy of the proposed algorithm is 0.11 m in weak NLOS environment and 0.17 m in strong NLOS environment respectively,which has a higher positioning accuracy compared with other algorithms and further reduces the requirement for UWB base station density.

关 键 词：超宽带 协同定位 迭代算法 滤波处理 K-means++ DV-HOP 

分 类 号：P228[天文地球—大地测量学与测量工程]