| 基于室内无线模型的穿墙损耗校正 |
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| 引用本文： | 赵亚楠,严天峰,伍忠东,高锐. 基于室内无线模型的穿墙损耗校正[J]. 宇航计测技术, 2020, 39(3): 63. DOI: 10.12060/j.issn.1000-7202.2019.03.12 |
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| 作者姓名： | 赵亚楠 严天峰 伍忠东 高锐 |
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| 作者单位： | 1、兰州交通大学 电子与信息工程学院，甘肃兰州 730070；;2、甘肃省高精度北斗定位技术工程实验室，甘肃兰州 730070；;3、甘肃省无线电监测及定位行业技术中心，甘肃兰州 730070 |
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| 基金项目： | 中国铁路总公司科技研究开发计划重大课题（2017X013-A）；甘肃省高等学校创新项目（2017C-09）；光电技术与智能控制教育部重点实验室（兰州交通大学）开放课题（KFKT2018-16）；兰州交通大学青年科学研究基金（2018003）；兰州交通大学-天津大学创新基金项目（2018062） |
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| 摘 要： | K-M（Keenan-Motley）模型将单墙固定损耗值相加来计算室内无线信号穿透多墙的总损耗值，存在较大误差。针对该问题选取多种室内场景分别进行连续波（Continuous Wave，CW）测试，对无线信号穿墙损耗的影响因素和变化规律进行分析，提出了一种基于人工神经网络的室内无线模型穿墙损耗校正方法，对预处理后的测试数据进行训练并建立预测模型。经验证该预测模型符合校正判别准则，在实际场景下具有良好的预测准确度。
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| 关 键 词： | K-M模型 连续波测试 人工神经网络 模型校正判别准则 |
Wall Penetration Loss Correction based on Indoor Wireless Propagation Model |
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| ZHAO Ya-nan,YAN Tian-feng,WU Zhong-dong,GAO Rui. Wall Penetration Loss Correction based on Indoor Wireless Propagation Model[J]. Journal of Astronautic Metrology and Measurement, 2020, 39(3): 63. DOI: 10.12060/j.issn.1000-7202.2019.03.12 |
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| Authors: | ZHAO Ya-nan YAN Tian-feng WU Zhong-dong GAO Rui |
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| Affiliation: | 1、School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;2、High-percision Positioning Technology Compass Engineering Laboratory of Gansu Province,Lanzhou 730070,China;3、Radio Monitoring and Technology Center of Positioning industry of Gansu Province,Lanzhou 730070,China |
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| Abstract: | K-M(Keenan-Motley)model adds the loss of the single wall to calculate the total loss of indoor wireless signal penetrating multiple walls,which exists errors .To solve this problem,a variety of indoor scenes are selected for CW (Continuous Wave)tests,and the influence factors and changing rules of wireless signal through the wall loss are analyzed.An indoor wireless model through the wall loss correction method based on artificial neural network is proposed.The test data are trained and then the prediction model is established.The experimental results show that the predicted model conforms to the model correction criterion and has good prediction accuracy in the actual indoor scenarios. |
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| Keywords: | K-M(Keenan-Motley)model CW(Continuous Wave)tests Artificial neural network Model calibration criteria |
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