We propose a novel cooperative positioning algorithm that fuses information from satellites and terrestrial wireless systems, suitable for GPS-challenged scenarios. The algorithm is fully distributed over an unstructured network, does not require a fusion center, does not rely on fixed terrestrial infrastructure, and is thus suitable for ad-hoc deployment. The proposed message passing algorithm, named hybrid sum-product algorithm over a wireless network (H-SPAWN), is described and analyzed. A novel parametric message representation is introduced, to reduce computational and communication overhead. Through simulation, we show that H-SPAWN improves positioning availability and accuracy, and outperforms hybrid positioning algorithms based on conventional estimation techniques.

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We propose a novel hybrid GNSS-terrestrial localization algorithm based on particle filter that fuses ranging data from both satellites and terrestrial receivers. The proposed positioning approach, named hybrid-cooperative particle filter (HC-PF), is fully distributed and allows both increased positioning availability and accuracy compared to GNSS-only localization in challenged scenarios. Moreover, simulation results based on a realistic indoor scenario show that the proposed solution outperforms several state of the art algorithms such as unscented Kalman filter and an approach based on belief propagation.

In this contribution we derive an expression of the Cramér-Rao bound for hybrid cooperative positioning, where GNSS information is combined with terrestrial range measurements through exchange of peer-to-peer messages. These results provide a theoretical characterization of achievable performance of hybrid positioning schemes, as well as allow to identify critical network configurations and devise optimized node placement strategies.

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