基于EKPF的超宽带室内动态目标定位算法  

UWB Indoor Dynamic Target Localization Algorithm Based on EKPF

作　　者：万伟雄 李晓东[1] WAN Weixiong;LI Xiaodong(School of Electronic and Information,Xi’an Polytechnic University,Xi’an Shaanxi 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《信息与电脑》2021年第24期39-42,共4页Information & Computer

摘　　要：针对室内环境中非视距(Non-Line of Sight,NLOS)是影响超宽带(Ultra-Wide Band,UWB)定位技术精度的主要因素,提出对UWB采集的原始数据进行扩展卡尔曼粒子滤波,实现对室内动态目标的精准定位。通过扩展卡尔曼滤波算法产生重要性密度函数,利用当前时刻的量测使粒子的分布更加接近后验概率分布。结果表明,扩展卡尔曼粒子滤波在定位精度上可以达到0.24790m,比扩展卡尔曼滤波、无迹卡尔曼滤波以及粒子滤波更精确,相对均方误差减少了约8%。Aiming at the fact that Non-Line of Sight(NLOS) is the main factor affecting the accuracy of Ultra-Wide Band(UWB)positioning technology in indoor environments,it is proposed to extend the Kalman particle filter to the original data collected by UWB to achieve accurate positioning of indoor dynamic targets.The importance density function is generated by the extended Kalman filter algorithm,and the measurement at the current moment is used to make the distribution of particles closer to the posterior probability distribution.The results show that the extended Kalman particle filter can reach a positioning accuracy of 0.247 90 m,which is more accurate than the extended Kalman filter,the unscented Kalman filter and the particle filter,and the relative mean square error is reduced by about 8%.

关 键 词：室内定位 动态目标 扩展卡尔曼粒子滤波 超宽带 

分 类 号：TN925[电子电信—通信与信息系统]