International audienceThis paper focuses on the computation of the generalized ambiguity function (GAF) of a multiple antennas multiple frequencies radar system (MAMF). This study provides some insights into the definition of resolution parameters of a MAMF radar system. It turns out that the range and azimuth resolutions are not the most suitable criteria to specify the MAMF radar resolution. Therefore a new set of resolution parameters is introduced like the resolution ellipse which expresses the resolution anywhere in the image plane or δ→max, (δ→min) which expresses the highest (lowest) bound of the spatial radar resolution. To point out the pertinence of our study, we illustrate it with a MAMF radar system built around GPS satellites. The effect of the radar system geometry on resolution is investigated. For several scenarios, the GAF and its numerical form, the point spread function (PSF), are computed and their results are compared

We use the Markov Random Field (MRF) model in order to segment sonar images, i.e. to localize the sea bottom areas and the projected shadow areas corresponding to objects lying on seafloor. This model requires on one hand knowledges about the statistical distributions relative to the different zones and on the other hand the estimation of the law parameters. The Kolmogorov criterion or the x2 criterion allow to estimate the distribution laws. The Estimation Maximization (EM) algorithm or the Stochastic Estimation Maximization (SEM) algorithm are used to determine the maximum likelihood estimate of the law parameters. Those algorithms are initialized with the Kmean algorithm. Results are showing on real sonar pictures.

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.