Exploring Tag-free RFID-based Passive Localization and Tracking via Learning-based Probabilistic Approaches

RFID-based localization and tracking has some promising poten-tials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localiz-ing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization prob-lem. To track a moving subject, we propose GMM based Hid-den Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demon-strate the effectiveness and accuracy of our approach