A New RSS Fingerprinting-Based Location Discovery Method Under Sparse Reference Point Conditions

With the increasing demand for indoor location-based services, the received signal strength fingerprinting-based localization algorithm has become a research focus due to its accuracy and low hardware requirements. However, how to achieve the accurate location discovery relies solely on the received signal strength under the sparse reference points condition, which is the main contribution of this paper. First, the Voronoi diagram is adopted to regionalize the positioning area and form a distributed signal propagation description, which can reduce the influence of environment interference. Second, aiming at the local motion tracking problem, a region-based location search model is constructed to achieve the initial position estimation and provide the motion model for the following optimization of location estimation. Third, in order to reduce the cumulative error caused by the environmental noise and the local optimum problem, the regularized particle filtering algorithm with map-correction is employed to implement the dynamic calibration of the particle updating equation. To verify the proposed algorithm, an indoor wireless experiment system is finally designed in this paper. The experiment results indicate that the proposed algorithm can increase the positioning accuracy by 28.2% compared with the fingerprinting-based localization algorithm when the RPs density is reduced to 0.2/ (0.5m*0.5m).