| 摘要(英) |
In recent years, there have been technological developments in many directions in the development of indoor positioning, and there are various solutions, and these solutions have their advantages and limitations, but high-precision positioning methods require higher unit price equipment for deployment. This study′s purpose is to design a new positioning algorithm to achieve the best balance between accuracy and equipment cost. With many new personal mobile devices such as mobile phones, wearable devices, etc., we can locate the field as long as the Bluetooth function of the device is turned on, and the application of sensing location can be more extensive.
This study uses 12 Bluetooth modules to be evenly arranged in the indoor space. These Bluetooth signals are used as the basis for machine learning training by personnel in the room. The TensorFlow learning framework is used to convolutional neural networks. The training and prediction methods are used to design the best model parameters, which greatly reduces the training model time. In the field of this research, the location of indoor personnel is judged by different Bluetooth signal strengths, and the positioning accuracy reaches 93.46%. |
| 參考文獻 |
參考文獻
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