Indoor geolocation using RF multipath with probabilistic data association

An indoor navigation problem is considered where the objective is real-time tracking of transponding tags in multipath environments using signals sent from and received at a set of radio frequency (RF) sources at fixed and known locations. Current systems depend on detection of direct path signals and treat multipath signals as spurious. We present an approach for exploiting the multipath signals to maintain tracking when direct paths are undetected. The method relies on estimating the parameters of minimum-complexity models of the indirect path lengths. A maximum of three parameters is required to model indirect path lengths arising from an arbitrary number of specular reflections off planar surfaces. A probabilistic data association filter (PDAF) is used to mitigate uncertainties arising from noise, closely-spaced path lengths, and path length crossovers. The method is tested via simulation using bandlimited signals synthesized from ray trace data. Performance is compared to an optimized direct path filter using Monte Carlo analysis. No prior knowledge of multipath parameters or indoor infrastructure is assumed, and measurements are restricted to time-of-arrival (TOA) only. The results indicate that the PDAF consistently outperforms the direct path filter when one or more direct paths are blocked.