High-Performance Indoor Localization with Full-Band GSM Fingerprints

Abstract

GSM trace mobile measurements are used to study indoor handset localization in an urban apartment setting. Nearest-neighbor, support vector machine (SVM), and Gaussian process classifiers are compared. A linear SVM is found to provide mean room-level classification efficiency near 100%, but only when the full set of GSM carriers is used. To our knowledge, this is the first study to use fingerprints containing all GSM carriers, and the first to suggest that GSM could be useful for very high-performance indoor localization.