Development Of Android Device Beacon Positioning System

• Main Authors: Hsiao,KUO-CHING, 蕭虢璟
• Other Authors: LIU,DON-GEY
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/4rfa7d

碩士 === 逢甲大學 === 資訊電機工程碩士在職學位學程 === 106 === The Global Positioning System (GPS), which is used for outdoor positioning and navigation, is fully developed. However, this positioning method is applied to outdoor positioning, and is limited by a large amount of power demand, high cost, and imperviousness of buildings. In some field, It is no more effective use on indoor positioning development. Therefore, the wireless technology with low power and independent base stations is born, such as: Beacon, LoRa, etc., the independent base station to set up the Internet of Things (IoT) architecture established by the low-power sensor, indoors Positioning (Beacon) and outdoor positioning (LoRa) make the application field more extensive, such as regional personnel monitoring and personnel search, to achieve the concept of smart city. The positioning mode established by the low-power wireless radio, whether Beacon or LoRa,use RSSI (Received Signal Strength Indicator) to distance conversion, and use the triangulation method to know the location of the person.Most positioning techniques are still due to RSSI, and the conversion distance is not accurate. So, this study uses iBeacon (wafer: DA14580) to send signals, collect signals of advert interval for analysis, filter out signals that may not be needed, and correct the distance converted by RSSI, and then develop indoor positioning of the App. The signal results from this study show that in the static data analysis, the base station and the receiving device are placed at 1 meter, and the advert interval is 100ms~900ms. It is found in the data of 200 signals that the peak and valley values can be clearly separated. In the 100ms advert interval data, if the signal is divided into one segment in the shortest time of 2 seconds, at least one peak data can be obtained, and the peak conversion distance will fall between 0.8 and 1.1 meters. In the dynamic data analysis, also use the same way , and the distance state curve of the base station and the receiving device can be obtained, and in this way, about 90% of the unnecessary signals can be removed. It can use the indoor positioning app and effectively get the location of the person.