| 融合小波变换与神经网络的RSSI室内测距算法 |
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| 引用本文： | 朱梦豪,卢小平,路泽忠,李亚平,陶晓晓.融合小波变换与神经网络的RSSI室内测距算法[J].测绘通报,2020,0(1):50-54. |
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| 作者姓名： | 朱梦豪 卢小平 路泽忠 李亚平 陶晓晓 |
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| 作者单位： | 河南理工大学矿山空间信息技术国家测绘地理信息局重点实验室, 河南 焦作 454000 |
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| 基金项目： | 国家重点研发计划（2016YFC0803103）；河南省高校创新团队支持计划（14IRTSTHN026） |
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| 摘 要： | 提出一种融合小波变换与神经网络的基于WiFi的RSSI室内测距算法，该方法通过小波变换与神经网络对RSSI数据、路径损耗模型进行修正。利用小波分解与单支重构方法，只对低频的近似部分进行单支重构，舍弃高频细节部分，同时使用神经网络训练特定环境下的路径损耗模型。通过实例验证表明，该算法最大测距误差、最小测距误差、平均测距误差分别为1.206、0.037、0.692 m；平均测距误差比路径损耗模型、BP神经网络模型分别提高了1.846、0.469 m。
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| 关 键 词： | 小波分解与单支重构 神经网络 路径损耗模型 RSSI 室内 |
| 收稿时间： | 2019-04-21 |
| 修稿时间： | 2019-06-24 |
RSSI indoor ranging algorithm based on wavelet transform and neural network |
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| ZHU Menghao,LU Xiaoping,LU Zezhong,LI Yaping,TAO Xiaoxiao.RSSI indoor ranging algorithm based on wavelet transform and neural network[J].Bulletin of Surveying and Mapping,2020,0(1):50-54. |
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| Authors: | ZHU Menghao LU Xiaoping LU Zezhong LI Yaping TAO Xiaoxiao |
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| Institution: | Key Laboratory of Mine Spatial Information and Technology of NASMG, Jiaozuo 454000, China |
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| Abstract: | A WiFi-based RSSI indoor ranging algorithm based on wavelet transform and neural network is proposed. The method is to correct the RSSI data and path loss model by wavelet transform and neural network. Using wavelet decom position and single-reconstruction reconstruction method, only a single-branch reconstruction of the approximate part of the low-frequency, discarding the high-frequency details, and using the neural network to train the path loss model in a speci fic environment. The example shows that the maximum ranging error, minimum ranging error and average ranging error of the algorithm are 1.206, 0.037 and 0.692 m. The average ranging error is 1.846 and 0.469 m compared with the path loss model and BP neural network model. |
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| Keywords: | wavelet decomposition and single-branch reconstruction neural network path loss model RSSI indoor |
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