基於接收訊號強度指示(RSSI)用於室內定位，並依據量測發射器到接收器的物體距離，但是由於無線訊號干擾嚴重，尤其是多路徑衰落，如何獲取準確的RSSI訊號強度是一項挑戰。同時，如何精確轉換RSSI與推估距離是非常關鍵的技術，為此我們提出一種用於精確室內定位的特徵模型(FM)。本次實驗設備分別使用 Gimbal 公司出品的 iBeacon 設備作為發射端，與 Raspberry Pi3 B+ 作為接收端，同時設立運端運算伺服器，收集實時RSSI訊號強度，並利用人工神經網路計算 iBeacon 發射器之所在位置。

Received signal strength indicator (RSSI) is used for indoor positioning and measures the transmitter-to-receiver object distance. However, due to severe radio signal interference, especially multi-path fading, how to obtain accurate RSSI signal is a challenge. At the same time, how to accurately convert the RSSI and estimate the distance is a very critical technology, for which we propose a fingerprint model (FM) for precise indoor positioning. The experimental equipment use the iBeacon device produced by Gimbal as the transmitting and the Raspberry Pi3 B+ as the receiving. At the same time, a terminal operation server was set up to collect real-time RSSI signal, and the position of the iBeacon transmitter was calculated using an Artificial Neural Network.

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