A Channel Adaptive WiFi Indoor Localization Method based on Deep Learning

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning technologies had emerged in the past decades, and WiFi-based approach is one of the most promising ones. However, the existing WiFi localization methods fail to take into account the disparate influence of packets transmitted in different channels so as to inhibit the further improvement of localization accuracy. Therefore, we present CADNN: a Channel Adaptive WiFi localization method based on Deep Neural Network (DNN). Specifically, a comprehensive analysis on signal attenuations in different channels along with error analysis based on Cramer-Rao Lower Bound (CRLB) is conducted in theory. Then, the channel set splitting scheme for practical localization to leverage multi-channel features is proposed. Finally, we design a localization framework using multi-objective regression DNN to adapt Received Signal Strength (RSS) measurements from different channel sets. The results from real-world experiments confirm the effectiveness of channel adaptive and show that CADNN can improve localization accuracy by at least 25.3% and 19.5% respectively on the two datasets and it can serve thousands users within one second.