Device-free wireless localization and activity recognition is an emerging technique, which could estimate the location and activity of a person without equipping him/her with any device. It deduces the state of a person by analyzing his/her influence on surrounding wireless signals. Therefore, how to characterize the influence of human behaviors is the key question. In this paper, we explore and exploit a radio image processing approach to better characterize the influence of human behaviors on Wi-Fi signals. Traditional methods deal with channel state information (CSI) measurements on each channel independently. However, CSI measurements on different channels are correlated, and thus lots of useful information involved with channel correlation may be lost. This motivates us to look on CSI measurements from multiple channels as a radio image and deal with it from the two-dimensional perspective. Specifically, we transform CSI measurements from multiple channels into a radio image, extract color and texture features from the radio image, adopt a deep learning network to learn optimized deep features from image features, and estimate the location and activity of a person using a machine learning approach. Benefits from the informative and discriminative deep image features and experimental results in two clutter laboratories confirm the excellent performance of the proposed system.

CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features