Indoor Localization System Based on WiFi RSSI using Wavelet-CNN Architecture

With the development of digitalization and informatization, the demand for location-based services has shown a significant growth trend. Indoor is the occasion where people stay the most, indoor localization based on WiFi RSSI is one of the most popular and efficient localization method. This paper proposed a WiFi indoor localization system, which combines convolutional neural network and wavelet transform. Specifically, we add wavelet transform to extract temporal features, and use one-dimensional convolutional neural network training fingerprint data to obtain localization result. In order to verify the effectiveness of the designed model, we set experiments on real fingerprint dataset. The experimental results show that the purpose system has great localization accuracy and stability.