Correlation-based radio localization in an indoor environment

We investigate the feasibility of using correlation-based methods for estimating the spatial location of distributed receiving nodes in an indoor environment. Our algorithms do not assume any knowledge regarding the transmitter locations or the transmitted signal, but do assume that there are ambient signal sources whose location and properties are, however, not known. The key idea is to compute pairwise cross correlations of the signals received at the nodes and use them to estimate the travel time between these nodes. If the ambient signal sources have sufficient space-time diversity, then the cross correlations recover the distance between the two receiving nodes. By doing this for all pairs of receivers we can construct an approximate map of their location using multidimensional scaling methods. We test this localization algorithm with measurement data from a radio measurement campaign using the Stanford broadband channel sounder in a cubicle-style office environment. Contrary to what is seen in other applications of cross-correlation methods, the strongly scattering nature of the indoor environment complicates distance estimation. However, using statistical methods, the rich multipath environment can be turned partially into an advantage by enhancing ambient signal diversity and therefore making distance estimation more robust. The main result is that with our correlation based statistical estimation procedure, assisted by multidimensional scaling, we were able to compute spatial antenna locations with an average error of about 2 meters, when the theoretical resolution limit is 1.25 meters.