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计算机系统应用:2020,29(5):175-181
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基于UKF的环境自适应UWB/DR室内定位方法
(浙江理工大学 机械与自动控制学院, 杭州 310018)
Adaptive UWB/DR Indoor Co-Localization Approach Based on UKF
(Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)
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投稿时间:2019-09-20    修订日期:2019-10-15
中文摘要: 针对复杂室内环境下超宽带(Ultra WideBand,UWB)信号传播的非视距(Non Line Of Sight,NLOS)误差问题,本文提出了一种基于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的环境自适应UWB/DR室内定位方法.该方法通过建立自适应UKF滤波模型,将UWB定位信息和航迹推算(Dead Reckoning,DR)定位信息进行融合.依据新息和高斯分布的3σ原则来对UWB定位结果进行非视距检测,再通过新息的实时估计协方差和理论协方差来构建环境适应系数,进而用此系数动态修正UWB定位的观测噪声,使得观测噪声自适应真实环境,降低NLOS误差对融合定位结果的影响.实验结果表明,该方法能有效减小UWB定位的NLOS误差,并且由于环境适应系数的创新引入,比UKF定位和粒子滤波定位(Particle Filtering,PF)有更高的定位精度和更强的抗NLOS误差性能.
中文关键词: UKF  环境适应系数  UWB/DR  原则  非视距检测
Abstract:In view of the Non-Line-Of-Sight (NLOS) error of Ultra-WideBand (UWB) signal propagation in complex indoor environments, an adaptive UWB/DR co-localization approach based on Unscented Kalman Filter (UKF) is proposed. It combines the positioning information of UWB and Dead Reckoning (DR) by establishing an adaptive UKF filtering model. In this process, the principle of innovation and Gaussian distribution is used to detect whether the UWB positioning result contains NLOS error, and then the environmental adaptation coefficient, which is constructed by real-time estimation covariance and theoretical covariance of the innovation, dynamically correct the observed noise of UWB and make it adaptive to the real environment to reduce the impact of NLOS error on the positioning result to a greater extent. The experimental results show that the proposed approach can effectively reduce the NLOS error of UWB positioning, and because of the innovative introduction of environmental adaptation coefficient, it has higher positioning accuracy and stronger anti-NLOS error performance than UKF positioning and Particle Filtering (PF) positioning.
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引用文本:
周琳,李晓明,江先志.基于UKF的环境自适应UWB/DR室内定位方法.计算机系统应用,2020,29(5):175-181
ZHOU Lin,LI Xiao-Ming,JIANG Xian-Zhi.Adaptive UWB/DR Indoor Co-Localization Approach Based on UKF.COMPUTER SYSTEMS APPLICATIONS,2020,29(5):175-181

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