Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter
Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter
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DOI：
10.1109/tim.2014.2335912
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发表时间：
2015-01-01
影响因子：
5.6
通讯作者：
Fourati, Hassen
中科院分区：
文献类型：
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作者：
Fourati, Hassen
针对室内环境下行人跟踪问题，提出了一种脚载零速度更新（ZVU）辅助惯性测量单元（IMU）滤波算法。算法输出是足部运动学参数，包括足部方向、位置、速度、加速度和步态相位。足部运动滤波算法结合了用于方向估计、步态检测和位置估计的方法。一种新的互补滤波器被引入到更好地预处理的传感器数据从一个脚安装的IMU包含三轴角速率传感器，加速度计，和磁力计，并估计脚的方向，而不诉诸全球定位系统数据。步态检测是使用一个简单的状态检测器完成的，该检测器基于加速度和角速率测量在状态之间转换。一旦计算出脚的取向，就使用积分加速度和速度数据来获得位置估计，该积分加速度和速度数据已经使用所实现的ZVU算法在跨步站立阶段针对漂移进行了校正，从而提高了位置精度。我们通过在典型的公共建筑中进行常规人类步行试验期间使用商业IMU来实验我们的发现。实验结果表明，该定位方法实现了约0.4%的位置精度，并提高了最近的文学作品的性能。
This paper proposes a foot-mounted zero velocity update (ZVU) aided inertial measurement unit (IMU) filtering algorithm for pedestrian tracking in indoor environment. The algorithm outputs are the foot kinematic parameters that include foot orientation, position, velocity, acceleration, and gait phase. The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation. A novel complementary filter is introduced to better preprocess the sensor data from a foot-mounted IMU containing triaxial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to global positioning system data. A gait detection is accomplished using a simple states detector that transitions between states based on acceleration and angular rate measurements. Once foot orientation is computed, position estimates are obtained using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvement. We show our findings experimentally by using of a commercial IMU during regular human walking trials in a typical public building. Experiment results show that the positioning approach achieves approximately a position accuracy around 0.4% and improves the performance regarding recent works of literature.