Research on Indoor Localization Method based on Deep Learning and Wireless Sensing

Aiming at the problems of indoor localization accuracy due to scale difference and noise interference in wireless sensing, a deep learning indoor localization architecture for wireless sensing is proposed. Firstly, iBeacon devices are arranged in the localization area to collect location fingerprints, and then stacked autoencoder (SAE) is introduced to effectively capture the depth features of the data to improve the robustness of the subsequent localization model. To further enhance the global temporal sensing capability of the model, a bidirectional long short-term memory network (BiLSTM) is introduced for location prediction. The experimental results show that the algorithm achieves higher positioning accuracy and better generalization performance than the traditional indoor localization methods.