  While a vast number of location-based services appeared lately, indoor
positioning solutions are developed to provide reliable position information in
environments where traditionally used satellite-based positioning systems
cannot provide access to accurate position estimates. Indoor positioning
systems can be based on many technologies; however, radio networks and more
precisely Wi-Fi networks seem to attract the attention of a majority of the
research teams. The most widely used localization approach used in Wi-Fi-based
systems is based on fingerprinting framework. Fingerprinting algorithms,
however, require a radio map for position estimation. This paper will describe
a solution for dynamic radio map creation, which is aimed to reduce the time
required to build a radio map. The proposed solution is using measurements from
IMUs (Inertial Measurement Units), which are processed with a particle filter
dead reckoning algorithm. Reference points (RPs) generated by the implemented
dead reckoning algorithm are then processed by the proposed reference point
merging algorithm, in order to optimize the radio map size and merge similar
RPs. The proposed solution was tested in a real-world environment and evaluated
by the implementation of deterministic fingerprinting positioning algorithms,
and the achieved results were compared with results achieved with a static
radio map. The achieved results presented in the paper show that positioning
algorithms achieved similar accuracy even with a dynamic map with a low density
of reference points.
