Sparsity-based Indoor Localization in Wireless Sensor Networks

Abstract

Location sensing is fundamental for supporting wireless communications services. We exploit the signal correlation structure observed in an indoor localization environment in order to provide accurate position estimation by means of a limited amount of measurements. We use a fingerprinting method based on the received signal strength indicator (RSSI). It consists in two phases: a training phase, where we build a database of RSSI, and a runtime phase, where we get the measurements and compute the estimated position.