Abstract
We propose new tools that allow one to perform statistical modeling of radar cross section (RCS). In our approach, we model fluctuations of RCS as a realization of nonstationary random process with a hidden time-varying state that governs its local properties. We describe how one can employ a recently proposed Bayesian tracker to fit the adopted model to an observed sequence of data, and explain how to validate the fitted model's goodness of fit. We apply the proposed approach to data recorded with the PaRaDe FM passive radar system. The results support our hypothesis that long integration times that are typically employed in FM passive radars result in smoother (less spiky) behavior of target RCS than predicted by the classical Swerling I and III models.
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