基于惯性传感器的动态步长算法研究

行人步态参数的精确估计是行人自主导航系统和行人健康监测的关键技术之一。针对当前行人自主导航系统中步长估算算法精度低和弱适应性的问题,提出了一种计算行人动态步长算法。首先对行人的步态特征进行分解,利用改进的零速检测确定行人运动状态,采用卡尔曼滤波技术降低惯性传感器中累积误差的影响,再对进行滤波和坐标转换后的加速度进行双重积分,最终得到行人脚尖的运动轨迹。通过采用MTI-700惯性模块设计实验并进行实验验证。结果表明,该文提出的步长算法计算的步长与行人实际步长的误差低于3.0%。与现有的行人动态步长算法相比,该算法首次计算出行人脚尖的运动轨迹,精度较高且适应强,在行人自主导航及行人健康监测领域具有较大的应用价值。

Study on Dynamic Step Length Algorithm Based on Inertial Sensor

Accurate estimation of the gait parameters is one of the key technologies in both pedestrian navigation system and pedestrian health monitoring. A calculation algorithm of pedestrian dynamic step length is proposed in this paper, which can solve the current problems of low accuracy and weak adaptability in pedestrian step length estimation algorithm. First, the pedestrians gait feature is decomposed, the improved zero velocity detection is used to determine the state of pedestrian movement. The Kalman filtering technology is utilized to reduce the influence of the cumulative error of the inertial sensors. Then the filtered and coordinate transformed acceleration is doubly integrated so as to get the trajectories of pedestrians. The algorithm is validated by experiments using a MTI 700 inertial sensor module. The traversed distance of the subjects were calculated with less than 3.0% error with respect to actual walking distance. The algorithm is of high precision and strong adaptation compared with the existing algorithms of pedestrian dynamic step length, it is the first time that the trajectory of pedestrian been calculated and it has great prospect in the application of pedestrian navigation field.