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This paper proposes an algorithm for estimating the fractal dimension of real sea bistatic synthetic aperture radar data. The algorithm is based on the use of the fractal dimension estimated by the box counting method to detect the sub-data which contain the targets. Based on this approach, the performance of the fractal detector proposed in this paper was demonstrated by the evaluation of the probability of detection by means of Monte Carlo simulation and was verified that we can use the fractal dimension to distinguish between targets and clutter, even for a small signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

This paper deals with the imaging of a moving target using a multifrequency and multistatic radar consisting in one receiver and several narrowband transmitters. Considering two hypotheses about the studied target, we derive two multistatic inverse synthetic aperture radar processors: the first one, which models the target as a set of isotropic points, performs a coherent sum of bistatic images; the second one, which models the target as a set of nonisotropic points, performs an incoherent sum of bistatic images. Numerical simulations are done, which demonstrate the efficiency of the second processor. We also apply both processors to a multistatic passive radar scenario for which the transmitters are FM stations located in a realistic configuration. We study the system performance in terms of resolution and sidelobe levels as a function of the number of transmitters and of the integration time. Both processors are applied to similar complex targets for which the scattered fields are simulated by a numerical electromagnetic code. The resulting multistatic radar images show interesting characteristics that might be used by classification algorithms in future work. [ABSTRACT FROM AUTHOR]