An Effective 3D Indoor Localization Approach Based on Fingerprint Fusion Positioning

We propose an effective target locating approach based on the fingerprint fusion positioning (FFP) method which combines the time-difference of arrival (TDOA) and the received signal strength in the stationary 3D scenarios. The FFP method fuses pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce new auxiliary parameters to estimate the target motion state. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and Bayesian frameworks. The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our approach.