摘要
DS-TWR是基于UWB的室内定位技术的主流测距方式。现有方法使用改进卡尔曼滤波算法,利用先验信息和观测点调整观测协方差可以提高定位精度,但在直接提高DS-TWR实时测距值精度方面的研究较少。为了直接减小测距信息中的随机误差,采用经验模型降低DS-TWR存在的天线时延误差,并且提出一种改进的自适应卡尔曼滤波,对得到的实时测距信息进行去噪,从而进一步提高实时测距精度。在真实环境下对该算法做了经验模型和卡尔曼滤波算法两组对照实验,结果表明使用改进后的算法得到的测距估计值与真实距离信息最贴近,且经过经验模型改良和自适应卡尔曼滤波改进后与原标准DS-TWR的误差值明显变小,在厘米级标准上进一步提升了DS-TWR算法的实时测距精度。
Double-sided Two-way Ranging(DS-TWR)is the mainstream ranging method based on UWB indoor positioning technology.Few studies have increased DS-TWR range accuracy.Researchers employ the enhanced Kalman filter to increase location accuracy.This work recommends employing an updated adaptive Kalman filter to denoise real-time range information to minimize antenna delay error in DS-TWR.Real-world testing compare Kalman filter with empirical model.The enhanced approach is more accurate than the empirical model and adaptive Kalman filter.The DS-TWR algorithm’s real-time ranging accuracy is better than the typical error value at the centimeter level.
出处
《工业控制计算机》
2023年第2期86-88,共3页
Industrial Control Computer
