An automatic virtual calibration of RF-based indoor positioning with granular analysis

Abstract

The positioning methods based received signal strength indicator (RSSI) is using the RSSI values to estimate the positions of the mobile. The RSSI positioning method based on propagation models, the system’s accuracy depends on the adjustment of the propagation models parameters. In actual indoor environment, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we propose and demonstrate an automatic virtual calibration technology of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose the low-complexity Gaussian Filter (GF), Virtual Calibration Technology (VCT), Probabilistic Positioning Algorithm (PPA), and Granular Analysis(GA) make the proposed algorithm robust and suitable for indoor positioning from uncertainty, self-adjective to varying indoor environment. Using MATLAB simulation, we study the calibration performance and system performance, especially the dependence on a number of system parameters, and their statistical properties. The simulation results prove that our proposed system is an accurate and cost-effective candidate for indoor positioning.