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Abstract-In this paper a hybrid ray tracing/statistical channel model for the ultra-wideband (UWB) frequency range is proposed. The conventional ray tracing model is complemented with randomly distributed point scatterers placed on the surface of large objects like walls. The wave propagation in such scenario is calculated in a deterministic way. The parameters of the scatterers are derived from the measurements of reflection from typical indoor walls.

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This paper focuses on an emergency situation in which a network of ultra-wide-band (UWB) sensor nodes mounted on moveable platforms is moving in a room for purposes of recognition of objects. The recognition is accomplished between 2D canonical objects, which are scanned by UWB Radar and a set of reference objects with same size which were analyzed by a ray-tracing method. The object recognition task is performed on the basis of RCS (radar cross section) measurements leading to corresponding radargram data. Here the objects characteristic geometric features are examined by using polar Fourier descriptors which are extended in a way that also provides for multiple reflections.

This paper presents a computationally effective approach for including dense multipath components in ray tracing simulations of ultra wideband (UWB) channels. Through a combination of a standard ray tracing model with a simple geometric-stochastic model realistic scenariospecific simulations are possible. The frequency and direction selectivity of the channel are reproduced accurately by the model. The structure and parameters of the stochastic part of the model are derived from measurements in the FCC-UWB frequency range. Compared to conventional ray tracing simulations the proposed model reduces considerably the differences between simulated and measured channel characteristics.