Abstract
Indoor localization algorithms have been proposed using various methods, such as angle of arrival, time of flight, and received signal strength (RSS). 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. The chapter discusses their use in self‐calibrating indoor localization systems. It reviews four kernel‐based localization algorithms and presents a common framework for their comparison. The chapter shows results from two simulations and from an extensive measurement data set, which provide a quantitative comparison and intuition into their differences. It compares the performance of the different kernel‐based localization algorithms and describes the environment along with the processing of the experimental data and the evaluation procedure for each data set.
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