摘要
新兴的物联网应用,对提供低成本、高精度的室内定位方案提出了巨大的需求。但是,在复杂的室内环境中很难使用独立的系统来达到更高要求的定位精度。为了解决传统的仅依赖于超宽带(Ultra-Wide Band,UWB)传感器的三基站观测距离最小二乘定位算法精度低和稳定性差的问题,提出了基于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的惯性测量单元(Inertial Measurement Unit,IMU)和UWB多传感器融合定位算法。此外,还提出了基于UKF的单基站观测距离和角度直接定位算法(Direct Position Algorithm,DPA),当定位精度要求不高时,DPA定位算法可以大大降低基站部署的复杂度和成本。结果表明所提出的三基站观测距离WLS定位算法定位精度相对而言有一定提高。基于UKF的融合定位算法在提高定位精度的同时还能够有效抑制定位数据的抖动,从而进一步地提高定位稳定性,适用于复杂室内环境下要求低成本、高精度和高可靠性的定位场景。
The emerging application of the Internet of things puts forward a huge demand for providing a low-cost and highprecision indoor positioning scheme. However, in the complex indoor environment, it is difficult to use an independent system to achieve higher requirements of positioning accuracy. In order to solve the problem of low accuracy and poor stability of the traditional three base station observation distance least square positioning algorithm which only relies on UWB(Ultra-wideband)sensors, an IMU(Inertial measurement unit) and UWB multi-sensor fusion positioning algorithm based on Unscented Kalman filter(Unscented Kalman filter) is proposed. In addition, the DPA(Direct position algorithm) based on UKF is also proposed. When the positioning accuracy is not high, the DPA positioning algorithm can greatly reduce the complexity and cost of base station deployment. The results show that the positioning accuracy of the proposed three base station observation distance WLS positioning algorithm is relatively improved. The fusion algorithm based on UKF can not only improve the positioning accuracy, but also effectively suppress the jitter of the positioning data, thus further improving the positioning stability. It is suitable for the positioning scene with low cost, high precision and high reliability in complex indoor environment.
作者
王春琦
孔祥琦
丁晓欢
卢忠青
陈常婷
WANG Chun-qi;KONG Xiang-qi;DING Xiao-huan;LU Zhong-qing;CHEN Chang-ting(Chengdu Monitoring Station of the State Radio Monitoring Center,Chengdu 610039,China;State Radio Monitoring Center Testing Center,Beijing 100041,China;Chengdu Aircraft Industrial(Group)Co.,Ltd,Chengdu 610091,China;College of Electronics and Information Engineering,Shenzhen University,Guangdong Shenzhen 518061,China;College of Big Data and Internet,Shenzhen Technology University,Guangdong Shenzhen 518118,China)
出处
《南昌航空大学学报（自然科学版）》
CAS
2020年第3期8-17,共10页
Journal of Nanchang Hangkong University(Natural Sciences)
关键词
物联网
超宽带
无迹卡尔曼滤波
惯性测量单元
internet of things
ultra-wideband
unscented Kalman filter
inertial measurement unit
