IMMKF与Chan-Taylor算法的协同定位

doi:10.16180/j.cnki.issn1007-7820.2023.12.001

摘要/Abstract

摘要：

TDOA(Time Difference of Arrival)测距方式是UWB(Ultra Wide Band)室内定位的常用方法,针对其不可避免的随机误差以及目标改变运动状态定位不准确的问题,文中提出了一种Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter)定位算法。该算法由Chan-Taylor加权算法与加入自适应算法IMM卡尔曼滤波算法组成,通过Chan-Taylor加权算法初次取得目标估计坐标,将该坐标值作为自适应算法IMM的卡尔曼滤波器的测量值,对目标坐标进行多次滤波处理,最终加权得到目标的最终估计坐标。实验将该算法与未滤波的Chan-Taylor加权算法、使用传统的卡尔曼滤波算法进行对比,结果显示该算法有效减小了系统的随机误差,并克服了传统卡尔曼滤波器在目标忽然改变运动状态时不能及时跟踪从而产生较大误差的问题,将误差标准差均值控制在15 cm之内。

关键词: 室内定位, 超宽带, TDOA, Chan算法, Taylor算法, 残差加权, 卡尔曼滤波, 交互式多模型

Abstract:

TDOA(Time Difference of Arrival) rangmg method is a typical opproach for UWB(Ultra Wide Band) indoor location.A Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter) localization technique is suggested in this study to address the unavoidable random error and inaccurate location of targets with changing motion states. With the addition of the adaptive algorithm IMM, the algorithm is made up of the Chan-Taylor weighting algorithm and the Kalman filter algorithm. The Chan-Taylor weighting procedure is used to acquire the target estimated coordinates for the first time. The coordinate value is then used as the measurement value for the Kalman filter of the adaptive algorithm IMM, and the target coordinates are filtered many times. The target's final estimated coordinates are provided by the final weighting. The experimental results reveal that the filtered Chan-Taylor weighting algorithm outperforms both conventional Kalman filtering and the unfiltered Chan-Taylor weighting algorithm. The algorithm successfully lowers the system's random error and fixes the issue that the conventional Kalman filter cannot track the significant error when the target abruptly changes its motion state,and the mean error standard deviation is controlled within 15 cm.

Key words: indoor location algorithm, ultra wide-band, TDOA, Chan algorithm, Taylor algorithm, RWGH, Kalman filtering, interacting multiple model

中图分类号:

TN929

王欣悦,余慧敏,胡露宁. IMMKF与Chan-Taylor算法的协同定位[J]. 电子科技, 2023, 36(12): 1-8.

WANG Xinyue,YU Huimin,HU Luning. Cooperative Localization of IMMKF and Chan-Taylor Algorithm[J]. Electronic Science and Technology, 2023, 36(12): 1-8.

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算法	最大误差	最小误差	平均误差
Chan-Taylor	96.335 00	1.081	28.448
Chan-Taylor-IMMKF	62.547 81	0.475	13.916