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RSS Based Indoor Localization Scheme Using GRNN and Virtual Grid-Points

Jung-Hyun Lee, Seung-Joo Lee, Youngil Park, Ki-Bang Yun, Ki-Doo Kim

Traditional RSS based self-localization for low cost Wireless Sensor Networks (WSN) suffers from lesser accuracy due to computational and memory resource limitations. In addition, an indoor wireless channel is influenced by so many factors that deriving the optimized propagation loss model for low cost WSN devices becomes difficult. In this paper, we present a flexible location estimation algorithm using a generalized regression neural network (GRNN) and virtual grid points, which is capable of learning the ever-changing wireless channel behaviour without the use of a specific propagation loss model.In the proposed scheme, a virtual grid concept is added to achieve a better refinement of the GRNN based algorithm and to overcome the disadvantages of conventional RSS based localization. The experimental results demonstrate that the proposed algorithm obtains a more precise location when compared to the conventional GRNN location estimation scheme