一种面向位置服务的超宽带室内定位算法

Location-oriented ultra-wide-band indoor positioning algorithm

超宽带技术由于较高的测距精度和穿透性能,对于位置服务有着重要的应用价值。在实际的高密度定位环境中,传统的定位算法受非视距误差和多径效应的影响,很难实时准确解算出实际位置坐标。虽然增加基站数量可以有效提高定位的精度,但是其成本也在不断提高。针对超宽带在高密度室内定位中实时性差、定位精度低的问题,提出了一种基于支持向量机的超宽带定位方法,提高了定位的精确性和鲁棒性;给出了基于到达时间差(TDOA,time difference of arrival)的支持向量机模型,重点在于将定位问题转化为分类问题的求解;通过TDOA值和坐标值来建立支持向量机分类模型,利用一对一分类模型实现了坐标值的解算,提高了坐标解算速度。仿真结果表明,在高密度实时定位中,相比于传统的Chan算法和Taylor算法,文中方法在定位精度近似的情况下,实时性要高于传统算法,满足实际定位中低功耗、快速高精度定位的要求。

Ultra-wide-band technology has important application value for location services due to its high ranging accuracy and penetration performance.In the actual high-density positioning environment,the traditional positioning algorithm is affected by non-line-of-sight error and multipath effect,and it is difficult to accurately calculate the actual position coordinates in real time.Although increasing the number of base stations can effectively improve the accuracy of positioning,its cost also increases.Aiming at the improvement of the accuracy and robustness of positioning,an ultra-wideband positioning method based on support vector machine was proposed to solve the problem of poor real-time performance and low positioning accuracy of ultra-wideband in high-density indoor positioning.A support vector machine model based on TDOA(TDOA,time difference of arrival)was given,with focus on transformation of the problem of location into the problem of classification.The support vector machine classification model was established by TDOA values and coordinate values.The one-to-one classification model was used to solve the coordinate values and improve the coordinate solution speed.The simulation results show that in the high-density real-time positioning,compared with the traditional Chan algorithm and Taylor algorithm,the method has higher real-time performance when the positioning accuracy is similar,which meets requirements for the actual positioning with its low power consumption,fast and high precision.