Research on improved PDR algorithm for underground personnel positioning
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摘要: 传统的行人航位推算（PDR）算法用于井下人员定位时，因步频检测、步长估计和航向估计阶段的姿态累计误差导致定位误差逐渐增大，而常用的零速校正、航向漂移消除、步态信号优化等误差修正方法无法改变PDR算法的固有缺陷，定位精度有待提高。提出采用改进的峰值检测法实现PDR算法中步频检测，基于深度循环神经网络（RNN）实现步长估计。将改进的PDR算法用于井下人员定位：首先采用手机加速度传感器、陀螺仪、磁力计获取行人运动数据；然后采用改进的峰值检测法获取固定时间间隔内的平均步频，与时间间隔、加速度及加速度方差作为特征输入训练后的深度RNN模型进行步长估计；最后结合估计的航向角预测人员当前位置。试验结果表明，改进的井下人员定位PDR算法对测试集数据的预测相对误差为5.9%，对实际测试路线的定位相对误差为1.6%~3.9%，小于传统PDR算法定位误差，有效提高了井下人员定位精度。Abstract: As the traditional pedestrian dead reckoning (PDR) algorithm being used for underground personnel positioning, the positioning error gradually increases due to the cumulative error of the step frequency detection, step length estimation and heading estimation phases. Moreover, the commonly used error correction methods such as zero speed correction, heading drift elimination and gait signal optimization cannot change the inherent defects of the PDR algorithm, and the positioning accuracy needs to be improved. It is proposed to use an improved peak detection method to achieve step frequency detection in the PDR algorithm, and to achieve step length estimation based on a deep recurrent neural network (RNN). The improved PDR algorithm is used for underground personnel positioning. Firstly, cell phone accelerometer, gyroscope and magnetometer are used to obtain pedestrian movement data. Secondly, the improved peak detection method is used to obtain the average step frequency in a fixed time interval. The average step frequency, the time interval, acceleration and acceleration variance are used as features to be input to the trained deep RNN model for step length estimation. Finally, the estimated heading angle is added to predict the current position of the personnel. The experimental results show that the improved PDR algorithm for underground personnel positioning has a relative error of 5.9% in predicting the test set data and has a relative error of 1.6%-3.9% in positioning the actual test route. The relative error is smaller than the positioning error of the traditional PDR algorithm and the proposed PDR algorithm has improved the accuracy of underground personnel positioning effectively.
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期刊类型引用(9)
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