|Title:||Indoor positioning based on channel frequency response and machine learning|
|Subject:||Indoor positioning systems (Wireless localization)|
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Electronic and Information Engineering|
|Pages:|| pages : color illustrations|
|Abstract:||In indoor positioning, CSI is one popular technology. At the first, this report describes the hardware and the software should be used in this report. And this report describes using the Hummingboard pro to finish the indoor positioning system. Then this report describes the indoor positioning based on the channel frequency response and support vector machine, then depends on the disadvantage that the program needs cost more time when the locate point is increasing, then using a convolution neural network to replace the support vector machine. Then this report describes one method using 1-dimensional data to product 2-dimensional data. This report describes and compares the result depend on the support vector machine and convolution neural network at a different number of the points and the different distances of the points. Then this report has tested the result when people move in the room. At last, this report put forward some ideas to improve the performance of the indoor positioning system such as using the united of SVM and CNN to replace CNN.|
|Rights:||All rights reserved|
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|5173.pdf||For All Users (off-campus access for PolyU Staff & Students only)||3.55 MB||Adobe PDF||View/Open|
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