| Summary: | 碩士 === 國立臺北科技大學 === 電子工程系 === 107 === With the advancement of technology, the combination of mobile devices and geographic information has developed a Location-Based Service (LBS). The most basic function of LBS is positioning. The accuracy of positioning also affects the application level of LBS. The higher the positioning accuracy, the wider the application level. The most common outdoor positioning is the Global Positioning System (GPS), and the indoor positioning is to make a positioning by receiving different Wi-Fi signal sources and receiving strengths, because the streets in the bustling metropolitan area tend to be too much. The dynamic environment of high-rise buildings and crowds of people makes the satellite signal and Wi-Fi signal strength subject to multipath effects, resulting in positioning errors ranging from tens of meters to hundreds of meters, and even unable to locate. This thesis wants to use Wi-Fi signal strength positioning and GPS cooperative operation to improve the overall accuracy. Wi-Fi positioning uses the signal pattern identification method, combined with the latitude and longitude collected by GPS, to establish a Wi-Fi signal database online, using various applications. The positioning and comparison of the machine learning techniques ( K Nearest Neighbor Algorithm, K – Means Clustering Algorithm and K Nearest Neighbor Algorithm combined with K – Means Clustering Algorithm) of the positioning system are performed.
The hardware equipment uses STMicroelectronics STM32F407ZET6 embedded development board, combined with UBlox NEO-6M GPS module and ESP8266 Wi-Fi module to study according to this design.
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