PDR algorithm for precise positioning of underground personnel based on LSTM personalized step size estimation
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摘要: 针对传统的行人航位推算(PDR)算法由于步长和航向累积误差导致定位精度较低，不能满足井下人员精准定位需求的问题，提出了一种基于长短时间记忆网络(LSTM)个性化步长估计的井下人员精准定位PDR算法。首先采集井下人员运动中的加速度、陀螺仪惯性信息，解算每一步运动距离构建步长数据，通过离线训练获得井下人员个性化步长估计LSTM模型；然后在在线预测阶段通过矿用本安智能手机实时采集加速度、陀螺仪、地磁等井下人员运动数据，分别采用步伐检测算法、个性化步长估计模型获得井下人员运动步伐及每一步的步长，利用卡尔曼滤波融合航向估计算法获得航向角；最后根据步长估计和航向角预测井下人员当前位置。在内蒙古鄂尔多斯市高头窑煤矿采集井下人员运动数据进行试验，结果表明：基于LSTM个性化步长估计的井下人员精准定位PDR算法对井下人员运动中的步伐检测精度为96.5%，步长预测精度为90%；在井下真实环境中的相对定位误差为2.33%，提高了煤矿井下人员定位的精度。Abstract: The traditional pedestrian dead reckoning (PDR) algorithm has low positioning precision due to the accumulated errors of step size and heading, which can not meet the requirements of precise positioning of underground personnel. In order to solve the problem, a PDR algorithm for precise positioning of underground personnel based on long short-term memory (LSTM) personalized step size estimation is proposed. Firstly, the acceleration and gyroscope inertia information in the movement of underground personnel is collected, and the movement distance of each step is calculated to construct step size data. The LSTM model of personalized step size estimation of the underground personnel is obtained through off-line training. Secondly, in the online prediction stage, the underground personnel movement data such as acceleration, gyroscope and geomagnetism are collected in real-time through the mine intrinsically safe smart phone. The underground personnel movement step and step size of each step are obtained by using the step detection algorithm and personalized step size estimation model respectively. The heading angle is obtained by using the Kalman filtering and heading estimation algorithm. Finally, the current position of underground personnel is predicted according to step size estimation and heading angle. In Inner Mongolia Ordos Gaotouyao Coal Mine, the underground personnel movement data is collected for testing, and the results show as follows. The PDR algorithm for precise positioning of underground personnel based on LSTM personalized step size estimation has a step detection precision of 96.5% and a step size prediction precision of 90%. The algorithm has a relative positioning error of 2.33% in the real underground environment, which improves the personnel positioning precision in coal mine.
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图 2 井下人员个性化步长估计LSTM模型框架
Figure 2. LSTM model framework of underground personnel personalized step size estimation
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图 5 井下人员个性化步长估计LSTM模型的输入
Figure 5. Input of LSTM model of underground personnel personalized step size estimation
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图 6 井下人员个性化步长估计LSTM模型的结构参数
Figure 6. Structural parameters of LSTM model of underground personnel personalized step size estimation
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图 8 不同步长估计算法误差分布对比
Figure 8. Comparison of error distribution of different step size estimation algorithms
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图 11 传统PDR算法与基于LSTM个性化步长估计的PDR算法在煤矿井下行走路径对比
Figure 11. Comparison of walk routes of traditional PDR algorithm and PDR algorithm based on LSTM personalized step size estimation in coal mine
表 1 井下人员个性化步长估计LSTM模型的超参数
Table 1 Super parameter of LSTM model of underground personnel personalized step size estimation
批量大小 激活函数 优化器 学习率 迭代次数 早停次数 损失函数 128 ReLU Adam 0.001 500 50 MSE 
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表 2 步伐检测算法结果
Table 2 Results of step detection algorithm
试验
次数试验者1 试验者2 试验者3 误检步数 准确率/% 误检步数 准确率/% 误检步数 准确率/% 1 3 94 1 98 2 96 2 2 96 0 100 6 88 3 1 98 0 100 1 98 4 0 100 0 100 7 86 5 1 98 1 98 1 98
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