Indoor Fingerprinting With Bimodal CSI Tensors: A Deep Residual Sharing Learning Approach

Wi-Fi-based indoor fingerprinting is attracting increasing interest in the research community due to the ubiquitous access in indoor environments. In this article, we propose ResLoc, a deep residual sharing learning-based system for indoor fingerprinting using bimodal channel state information (CSI) tensor data. The proposed ResLoc system employs CSI tensor data, including the angle of arrival and amplitude, collected from a small set of training locations with known coordinates to train the proposed dual-channel deep residual sharing learning model. The proposed new model extends the traditional deep residual learning model by incorporating two or more channels and let the channels exchange their residual signals after each residual block. Unlike prior deep-learning-based fingerprinting schemes, ResLoc only requires for training one group of weights for all the training locations. The proposed ResLoc system is implemented with commodity Wi-Fi devices and evaluated with extensive experiments in three representative indoor environments. The experimental results validate that the proposed ResLoc system can achieve high localization accuracy using a single Wi-Fi access point in indoor environments.