A study on outdoor localization method based on deep learning using model-based received power estimation data of low power wireless tag

抄録

We are developing a method to acquire position information of a cow outdoors using Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE). As existing research, there is a localization method using fingerprint database as learning data in deep learning. However, that method has the problem that it costs to create a database by measurement in a vast outdoor environment. Therefore, we considered to build a part of the fingerprint database using virtual space modeling received power measurement environment in a pasture. Experimental results showed that an average distance error to GPS data is about 6 m by training DNN using the database and additionally training DNN using actual GPS data.