Automatic building and floor classification using two consecutive multi-layer perceptron

Key issues of indoor localization is taking full advantages and overcoming its disadvantages. Indoor localization based on Wi-Fi fingerprinting attracts researchers’ attentions since it does not require new infrastructure and devices. Many devices such as smart phones and laptops, which have a function to capture Wi-Fi signals, can be used for Wi-Fi fingerprinting. However, due to unreliable Wi-Fi signals, there are still difficulty to achieve high positioning accuracy. The unreliable signal disturbs devices to find their locations. As a result, getting localization with devices sometimes makes a wrong decision in building classification. It is useless for people to find a destination floor if they are in different building. In this paper, we propose two consecutive multi-layer perceptrons to get more precise localization. With sumple structure, we get better performance and show precise decision results in building classification, which is critical in Wi-Fi fingerprinting. We use UJIndoorLoc dataset which is open dataset.