基于角度的HDE算法在室内行人航迹定位中的研究

Researching on Indoor Pedestrian Trajectory Based on Angle HDE Algorithm

针对现有启发式偏移消除算法HDE(Heuristic Drift Elimination)中航向角推算不准确、反馈系数鲁棒性较差的问题,提出了对经过扩展卡尔曼滤波的航向角进行启发式漂移消除的算法AHDE(Angle Heuristic Drift Elimination)。首先利用扩展卡尔曼滤波EKF(Extended Kalman Filter)融合陀螺仪、加速度信息,通过四元数的更新来估计航向角,再用启发式漂移消除算法对航向角进行修正,最后结合步数及步长信息推算出行人的行走轨迹。实验结果显示,在行走方向较为固定的典型室内环境中,行走距离在250 m时,该算法平均误差不超过2 m,而HDE算法误差会达到4 m左右。并且该系统具有比对陀螺仪数据进行启发式漂移消除系统更强的鲁棒性。当以100 Hz频率读取数据时,AHDE算法反馈系数的选择范围由HDE算法的[0.001,0.028]扩展为[0.005,0.23],几乎扩大了一个数量级。

The Heuristic Drift Elimination（ HDE） algorithm has disadvantages such as the inaccurate heading angle and poor robustness,the AHDE algorithm was proposed which implementing Heuristic Drift Elimination based on heading angle with Extended Kalman Filter. The heading angle is renewed by Quaternion which is achieved from the integration of gyroscope data and accelerometer data. After updating the heading angle with the algorithm of Heuristic Drift Elimination,the angle can be used to reckon the pedestrian trajectory combined with step number and stride length. The experimental results show that,when walking in typical structured indoor environments,the error of the proposed algorithm is less than 2 m in 250 m distance,and the error of the HDE algorithm is 4 m roughly. On the other hand,when the sampling data rate of the sensors is 100 Hz,the convergence range of feedback coefficient of the AHDE system can be extended to[0.005,0.23],however the range of the HDE system is[0.001,0.028]. That is to say the AHDE system is more robust than the HDE system.