Position prediction of underground moving targets in mines based on IPSO-LSTM
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摘要:
提升井下人员定位精度能够加强矿山安全监测，最大程度保障井下人员的生命安全。针对现有测距类算法受现场环境影响致使定位精度不足的问题，提出一种基于IPSO-LSTM的定位模型，应用于井下动目标的位置预测。采用LSTM构建指纹定位模型，通过UWB无线模块采集距离信息以构建距离-位置指纹关系数据库，利用数据库对PSO-LSTM模型进行训练，最后将训练好的模型进行目标轨迹预测。为比较不同改进策略对PSO的提升效果，对比了混沌映射随机初始化种群位置、非线性惯性权重递减、非对称优化学习因子和适应度函数优化4种改进策略，实验证明改进的PSO优化算法收敛速度快、鲁棒性好。为验证IPSO-LSTM的定位效果，以平均定位误差作为评价指标，将IPSO-LSTM模型与Chan算法、PSO-LSTM模型、LSTM神经网络、SSA-LSTM模型和GWO-LSTM进行对比，结果显示，IPSO-LSTM定位模型的平均定位误差为30 mm，相对传统Chan算法、LSTM、PSO-LSTM模型分别提升了76%、49%、24%。为降低局部误差偏大的现象，采用中值滤波对输入信息处理，进一步提升了定位精度。研究对进一步提高现有井下动目标定位系统的精度和稳定性具有重要意义和参考价值。
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关键词:
- 井下动目标 /
- 改进的粒子群优化算法 /
- IPSO-LSTM模型 /
- 平均定位误差
Abstract:Improving the positioning accuracy of underground personnel can not only strengthen mine safety monitoring, but also increase the speed of rescue, thus ensuring the life safety of underground personnel to the maximum extent.This paper proposes a positioning model based on IPSO-LSTM for position prediction of underground moving targets in response to the problem of existing ranging algorithms which are affected by the on-site environment, resulting in insufficient positioning accuracy.This article uses LSTM to build a fingerprint positioning model.It collects distance information through the UWB wireless module to build a distance-position fingerprint relationship database, which is used to train the PSO-LSTM model.Then we use the trained model to predict target trajectories.We compared four improvement strategies on PSO including random initialization of population position by chaotic mapping, nonlinear inertia weight reduction and fitness function optimization.Experiments show that the improved PSO optimization algorithm in this paper exhibit fast convergence speed and good robustness.In order to verify the positioning effect of IPSO-LSTM, we compared the IPSO-LSTM model with the Chan algorithm, PSO-LSTM model, LSTM neural network, SSA-LSTM model and GWO-LSTM.The average positioning error is used as the evaluation index.The results show that the average positioning error of the IPSO-LSTM positioning model proposed in this study is 30mm, which is 76% higher than the traditional Chan algorithm, 49% higher than the LSTM, and 24% higher than the PSO-LSTM model.In order to reduce large local errors, we used median filtering to process input information, further improving positioning accuracy.This study offers references for improving the accuracy and stability of the existing underground moving target positioning system.
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图 4 不同优化策略的惯性权重变化情况
Figure 4. Changes in inertia weight of different optimization strategies
图 5 指纹定位技术模型
BS(r)—第r个基站；(xm，ym)—第m个标签所在的位置；Dmr—第m个标签到第r个基站的距离
Figure 5. Model of fingerprint positioning technology
图 8 不同优化算法迭代曲线对比
Figure 8. Comparison of iteration curves under different optimization algorithms
图 12 中值滤波前后的距离信息对比
Figure 12. Comparison of distance information before and after median filtering
表 1 改进PSO优化LSTM模型的平均误差
Table 1. The average error of the three algorithmic models
算法 平均误差/mm a-PSO-LSTM模型 39.5 b-PSO-LSTM模型 38.3 c-PSO-LSTM模型 37.9 d-PSO-LSTM模型 32.9 PSO-LSTM模型 39.7 
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表 2 6种算法模型的平均误差
Table 2. The average error of six algorithmic models
定位算法 平均误差/mm 初始学习率 隐含单元个数 Chan算法 127 — — LSTM 58.7 0.1 70 SSA-LSTM 42.8 0.032 25 GWO-LSTM 46.5 0.15 113 PSO-LSTM 39.7 0.018 95 IPSO-LSTM 30.1 0.022 241
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