Abstract: An improved tracking and localization algorithm for mobile stations based on Extended Kalman Filtering(EKF) is proposed. In this algorithm, EKF is used to obtain multiple tracking trajectories of a mobile station with its initial position estimation being obtained. Combining the technique of removing the trajectories with larger deviations and data fusion with weighted averaging, a better trajectory among the trajectories is found. Based on this, a distance threshold is coordinated with the matching management of the better trajectory’s points for smoothing the better trajectory to obtain a best tracking trajectory. Simulation results show that the algorithm has low computational complexity, strong robustness as well as higher localization accuracy compared with traditional EKF tracking and localization algorithms.

Key words: wireless localization, mobile station, trajectory tracking, Extended Kalman Filtering(EKF), data fusion, distance threshold

摘要： 提出一种利用扩展卡尔曼滤波(EKF)算法实现移动台跟踪定位的改进算法。该算法在已获得移动台初始位置估计的基础上，利用 EKF对移动台的运动轨迹进行多次估计，获取多条跟踪轨迹，剔除偏差较大的轨迹并进行加权平均的数据融合处理，获取一条较优轨迹。再结合距离门限值对较优轨迹的点迹进行匹配管理，实现对较优轨迹的平滑处理，获得最优跟踪轨迹。仿真结果表明，该算法计算复杂度低、鲁棒性强，定位精度明显高于传统EKF跟踪定位算法。

关键词: 无线定位, 移动台, 轨迹跟踪, 扩展卡尔曼滤波, 数据融合, 距离门限

CLC Number: 

• TP312