Preliminary Study of Classifier Fusion Based Indoor Positioning Method

Abstract

Indoor positioning technology is commercially available now, however, the positioning accuracy is not sufficient in the current technologies. Currently available indoor positioning technologies differ in terms of accuracy, costs and effort, but have improved quickly in the last couple of years. It has been actively conducted research for estimating indoor location using RSSI (Received Signal Strength Indicator) level of Wi-Fi access points or BLE (Bluetooth Low Energy) tags. WiFi signal is commonly used for the indoor positioning technology. However, It requires an external power source, more setup costs and expensive. BLE is inexpensive, small, have a long battery life and do not require an external energy source. Therefore, by adding some BLE tags we might be able to enhance the accuracy inexpensive way. In this paper, we propose a new type of indoor positioning method based on WiFi-BLE fusion with Fingerprinting method. WiFi RSSI and BLE RSSI are separately processed each one by a Naive Bayes Classifier. Then, Multilayer Perceptron(MLP) is used as the fusion classifier. Preliminary experimental result shows 2.55m error in case of the MLP output. Since the result is not as good as the ones using conventional method, further test and investigation needs to be performed.

Survey on WiFi‐based indoor positioning techniques

“…There are also some studies on fingerprint positioning based on WiFi and bluetooth integration. For example, Miyashita et al [26] propose a WiFi-BLE fusion location system that uses a multi-layer perceptron as a fusion classifier, rather than a system that generates a wireless map using only WiFi signals. The system in [27] creates 3D fingerprint database based on the RSS of WiFi and bluetooth, and the fusion of optical sensor 3D coordinate and magnetic sensor rotational attributes.…”

Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents

“…Such technologies are continuously evolving, and it is predicted that they will have a big impact in coming years. For example, VO and agent technology [26,27], WSN [28], information fusion [29], indoor locating systems [30], etc.…”