Wi-Fi CSI fingerprinting-based indoor positioning using deep learning and vector embedding for temporal stability

Abstract

Fingerprinting systems based on channel state information (CSI) often rely on updated databases to achieve indoor positioning with high accuracy and resolution of centimeter-level. However, regularly maintaining a large fingerprint database is labor-intensive and computationally expensive. In this paper, we explore the use of deep learning for recognizing long-term temporal CSI data, wherein the site survey was completed weeks before the online testing phase. Compared to other positioning algorithms such as time-reversal resonating strength (TRRS), support vector machines (SVM), and Gaussian classifiers, our deep neural network (DNN) model shows a performance improvement of up to 10% for multi-position classification with centimeter-level resolution. We also exploit vector embeddings, such as i-vectors and d-vectors, which are traditionally employed in speech processing. With d-vectors as the compact representation of CSI, storage and processing requirements can be reduced without affecting performance, facilitating deployments on resource-constrained devices in IoT networks. By injecting i-vectors into a hidden layer, the DNN model originally for multi-position localization can be transformed to location-specific DNN to detect whether the device is static or has moved, resulting in a performance boost from 75.47% to 80.62%. This model adaptation requires a smaller number of recently collected fingerprints as opposed to a full database.