Robust indoor location estimation of stationary and mobile users

We present algorithms for estimating the location of stationary and mobile users based on heterogeneous indoor RF technologies. We propose two location algorithms, selective fusion location estimation (SELFLOC) and region of confidence (RoC), which can be used in conjunction with classical location algorithms such as triangulation, or with third-party commercial location estimation systems. The SELFLOC algorithm infers the user location by selectively fusing location information from multiple wireless technologies and/or multiple classical location algorithms in a theoretically optimal manner. The RoC algorithm attempts to overcome the problem of aliasing in the signal domain, where different physical locations have similar RF characteristics, which is particularly acute when users are mobile. We have empirically validated the proposed algorithms using wireless LAN and Bluetooth technology. Our experimental results show that applying SELFLOC for stationary users when using multiple wireless technologies and multiple classical location algorithms can improve location accuracy significantly, with mean distance errors as low as 1.6 m. For mobile users we find that using RoC can allow us to obtain mean errors as low as 3.7 m. Both algorithms can be used in conjunction with a commercial location estimation system and improve its accuracy further.