Wi-Fi DSAR: Wi-Fi based Indoor Localization using Denoising Supervised Autoencoder

In recent years, the demand for Wi-Fi has grown exponentially, which has led to the rapid development of indoor positioning services based on Wi-Fi fingerprints. Due to the Received Signal Strength Indicator (RSSI) variation over time, the device heterogeneity, and dynamic changes in the environment, the accuracy and robustness of traditional Wi-Fi fingerprint-based methods usually degrade. To alleviate these issues, we propose an approach based on the denoising supervised autoencoder for regression tasks that we named Wi-Fi DSAR. The idea of Wi-Fi DSAR design is to (a) has strong resistance to the noise in the received Wi-Fi signal, and (b) prevent overfitting the fingerprint database to achieve robustness. We do a performance study by comparing other fingerprint-based works with our Wi-Fi DSAR. All works are evaluated in three open datasets, i.e., DSI, IPIN2016, and IPIN2020, with different area sizes and access point densities. Experimental results demonstrate that compared with existing fingerprint-based methods, our Wi-Fi DSAR is able to reduce the average positioning error by 20% to 50%. This is because our Wi-Fi DSAR has both noise immunity and generalization performance.