To achieve the concrete aim of improving the positioning accuracy for large-scale indoor space in this thesis, we propose a weighted Gaussian
and Mean hybrid filter (G-M filter) to obtain the G-M mean of received signal strength indicator (RSSI) measurements, which is implemented by taking the practically experimental measurements of received signal strength indicator and analyzing the characteristics of received signal strength indicator. Meanwhile, various path loss models have been utilized to estimate the separation between the transmitting antenna and
the receiver (T-R separation) by calculating the G-M mean of received signal strength indicator, therefore, a dynamic-parameter path loss model has been proposed which can be appropriate to enhance the accuracy of estimated T-R separation and accurately describe the indoor position. Moreover, an improved fingerprint positioning has been proposed as the basic method combined with our tetrahedral trilateration scheme to reduce the positioning error of a large-scale 3D
indoor space which can achieve the average localization error of 1.5 meters.