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We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

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Abstract-We introduce a probabilistic language and a fast inference algorithm for state estimation in hybrid dynamic relational domains with an unknown number of objects. More specifically, we apply Particle Filters to distributional clauses. The particles represent (partial) interpretations of possible worlds (with discrete and/or continuous variables) and the filter recursively updates its beliefs about the current state. We use backward reasoning to determine which facts should be included in the partial interpretations. Experiments show that our framework can outperform the classical particle filter and is promising for robotics applications.

This work presents an analysis of the applicability of synthetic aperture radar (SAR) interferometry to landslide monitoring. This analysis was carried out by using different interferometric approaches, different spaceborne SAR data (both in the C-band and in the X-band), and in situ global navigation satellite system (GNSS) measurements. In particular, we investigated both the reliability of displacement monitoring and the issues of the cross-comparison and validation of the interferometric synthetic aperture radar (InSAR) results. The work was focused on the slow-moving landslide that affects a relevant part of the urban area of the historical town of Assisi (Italy).A C-band ENVISAT advanced synthetic aperture radar (ENVISAT ASAR) dataset acquired between 2003 and 2010 was processed by using two different interferometric techniques, to allow cross-comparison of the obtained displacement maps. Good correspondence between the results was found, and a deeper analysis of the movement field was possible. Results were further compared to a set of GNSS measurements with a 7 year overlap with SAR data. A comparison was made for each GNSS marker with the surrounding SAR scatterers, trying to take into account local topological effects, when possible.Further, the high-resolution X-band acquired on both ascending and descending tracks by the COSMO-SkyMed (CSK) constellation was processed. The resultant displacement fields show good agreement with C-band and GNSS measurements and a sensible increase in the density of measurements.