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Open Access Research

Correlation-based radio localization in an indoor environment

Thomas Callaghan1, Nicolai Czink2*, Francesco Mani3, Arogyaswami Paulraj4 and George Papanicolaou4

Author Affiliations

1 Rice University, Houston, Texas, USA

2 FTW Forschungszentrum Telekommunikation Wien, Austria

3 Université catholique de Louvain, Belgium

4 Stanford University, Stanford, USA

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EURASIP Journal on Wireless Communications and Networking 2011, 2011:135  doi:10.1186/1687-1499-2011-135

Published: 21 October 2011


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 motivation for this kind of node localization is to avoid interaction between nodes. It is most useful in non-line-of-sight propagation environments, where there is a lot of scattering. Correlation-based node localization is able to exploit an abundance of bandwidth of ambient signals, as well as the features of the scattering environment. 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. 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 in a cubicle-style office environment based on both ray-tracing simulations, and measurement data from a radio measurement campaign using the Stanford broadband channel sounder. 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 applied to the real data, assisted by multidimensional scaling, we were able to compute spatial antenna locations with an average error of about 2 m and pairwise distance estimates with an average error of 1.84 m. The theoretical resolution limit for the distance estimates is 1.25 m.

indoor localization; sensor networks; signal correlations; rich scattering; multidimensional scaling