Kernel Methods for RSS-based Indoor Localization

Abstract: This chapter explores the features and advantages of kernel-based localization. Kernel methods simplify received signal strength (RSS)-based localization by providing a means to learn the complicated relationship between RSS measurement vector and position. We discuss their use in self-calibrating indoor localization systems. In this chapter, we review four kernel-based localization algorithms and present a common framework for their comparison. We show results from two simulations and from an extensive measurement data set which provide a quantitative comparison and intuition into their differences. Results show that kernel methods can achieve an RMSE up to 55% lower than a maximum likelihood estimator.