针对全球导航卫星系统(Global Navigation Satellite System,GNSS)在受限空间内定位精度差,甚至无法定位和无线超宽带(Ultra-Wide Band,UWB)脉冲室内定位技术在非视距(Non Line of Sight,NLoS)环境下定位精度差以及定位稳定性低等问题,提出以误差状态卡尔曼滤波(Error State Kalman Filter,ESKF)为基础并将UWB定位技术和惯性传感器(Inertial Measurement Unit,IMU)技术相融合,设计一套UWB弱信号环境下的UWB定位算法模型,实现UWB弱信号环境下的厘米级定位.通过优化传统ESKF方法以及融合UWB和IMU的测量数据,解决传统UWB定位在NLoS环境下定位精度较差和定位结果容易发生偏移等问题.实验研究结果表明,在导航实验室内,系统在东-北-天(ENU)坐标系中东方向、北方向轴和OXY平面上的精度分别提高了 2.87％、12.02％和5.71％,方差分别降低了 5.80％、18.06％和5.71％.在地下通道UWB弱信号环境下,系统在东方向、北方向和OXY平面上的精度分别提高了 12.08％、24.10％和16.08％;方差分别降低了 8.12％、32.74％和12.23％.所提出的算法模型有效改善了 UWB室内定位技术在NLOS情况下,定位精度低、系统定位稳定性差的问题,降低了定位成本,具有很强的实用性.
High-precision Positioning Algorithm Based on UWB and IMU Fusion in UWB Weak Signal Environment
To solve the problems that the Global Navigation Satellite System(GNSS)has poor positioning precision in confined space and even cannot locate,and the Ultra-Wide Band(UWB)indoor positioning technology has poor positioning precision and low positioning stability in Non Line of Sight(NLoS)environment.Based on the error state Kalman filter and the fusion of UWB positioning technology and Inertial Measurement Unit(IMU),a UWB positioning algorithm model in UWB weak signal environment is designed to realize centimeter-level positioning in UWB weak signal environment.By optimizing the traditional error state Kalman filter method and fusing the measurement data of UWB and IMU,the problems of poor positioning precision and easy deviation of positioning results of traditional UWB positioning in NLoS environment are solved.The experimental results show that in the navigation laboratory,the precision of the system in the east direction,north axis and OXY plane in the East-North-Sky(ENU)coordinate system is increased by 2.87％,12.02％and 5.71％respectively,and the variance is reduced by 5.80％,18.06％and 5.71％respectively.In the underground channel UWB weak signal environment,the precision of the system in the east direction,north direction and OXY plane is improved by 12.08％,24.10％and 16.08％respectively.The variance is reduced by 8.12％,32.74％and 12.23％,respectively.The proposed algorithm model effectively improves low positioning precision and poor system positioning stability of UWB indoor positioning technology in the case of NLoS,reduces the positioning cost,and has strong practicability.
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