Mobile Sensor Location Optimization U sing Support Vector Machines with Error-Correcting Output Codes
Mobile Sensor Location Optimization U sing Support Vector Machines with Error-Correcting Output Codes
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DOI：
10.1109/wsce49000.2019.9040991
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Sharif H. R. Khalil;N. Namazi;Ouyang Feng
中科院分区：
文献类型：
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作者：
Sharif H. R. Khalil;N. Namazi;Ouyang Feng
本研究致力于引入并开发一种技术，以便在具有足够旁瓣射频（RF）信号功率的位置对移动传感器（MS）进行优化定位。所提出的方法包括生成下行链路发射信号不同方位角的旁瓣功率分布数据库（DB）。随后，利用生成的数据库对一个机器学习（ML）多类分类器以及两个不同的卷积神经网络（CNN）进行训练和测试，以确定所需的移动传感器位置。进行的仿真实验表明，对于8个不同的接收器位置，最大准确率分别达到99.25%、96.56%和96.10% 。
This work is concerned with the introduction and development of a technique to optimally position a Mobile Sensor (MS) in a location with adequate side lobe Radio Frequency (RF) signal power. The proposed method involves the generation of a database (DB) of side lobe power distribution for different azimuth angles of the downlink transmitted signal. The generated DB is subsequently used to train and test a Machine Learning (ML) multiclass classifier, as well as two distinct Convolution Neural Networks (CNN), to identify the desired MS location. Simulation experiments are performed which indicate a maximum accuracy of 99.25%, 96.56% and 96.10% for 8 different receiver locations.