Enhanced Gaussian mixture model of RSSI purification for indoor positioning

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging due to wireless interferences, especially multipath decline. Therefore, RSSI purification is critical so we propose an Enhanced Gaussian Mixture Model (EGMM) for accurate indoor positioning. EGMM improves Gaussian filter based on Gaussian Mixture Model (GMM) to identify the desirable Light of Sight (LOS) signals from multipath decline signals. EGMM integrates GMM with Akaike information criterion (AIC) to determine the best K value for GMM and filter the LOS signal set accurately. The experiment is conducted with iBeacon devices, and the average error distance of EGMM can be enhanced as 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 m, which is adequate for indoor positioning.