Dictionary learning based fingerprinting for indoor localization

Indoor localization is often challenging due to the non-availability of GPS signals. Recently, various radio frequency fingerprinting techniques have been proposed to identify indoor locations using simply received signal strength (RSS) measurements. In general however, RSS measurements are time-varying and are difficult to model for complex environments. This paper proposes the use of dictionary learning (DL) to generate high quality fingerprints that depend also on the channel characteristics for each location. An enhanced DL algorithm is proposed that utilizes prior information about the channel distribution, and can generate the fingerprints in an online fashion. Simulation results demonstrate the efficacy of the proposed approach.