| Summary: | 碩士 === 國立交通大學 === 土木工程系所 === 106 === In this paper, we focus on outdoor material positioning. Due to the development of wireless sensor technology, outdoor positioning and indoor positioning are widely popular with research scholar. Outdoor positioning are usually based on GPS(Global Positioning System) but the positioning error of mobile phone fall between 5 to 10 meters. Most of existing literature are using beacon in indoor positioning. It use high density and short distance transmission to estimate the distance between transmitter and receiver. Because there is a little literature about using beacon in outdoor positioning, we try to use beacon in outdoor positioning in this paper. BLE(Bluetooth Low Energy) is one of the most common positioning technology how it works is measuring the signal strength to estimate its distance between transmitter and receiver. In this paper we use collection of signal strength data with temperature and relative humidity to build a regression model. We also try back-propagation of supervised neural network to reduce the error of estimating the distance between transmitter and receiver from regression model, and we get 0.426(m) average error with 89.6% accuracy. Subsequently, we use three known coordinates of beacons in triangulation algorithm to find out the coordinate of phone. In this paper, we use Bayesian Regularization of back-propagation neural network to forecast coordinates of phone, and we get 0.6356 (m) average error better than 2.143(m) average error of using regression model.
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