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An open problem of along-track interferometry (ATI) for synthetic aperture radar (SAR) sensing of ocean surface currents is the need of ancillary wind information for inversion of Doppler centroid measurements, that have to be compensated for the propagation velocity of advancing and/or receding Bragg scatterers. We propose three classes of estimators which exploit multibaseline (MB) ATI acquisition and Doppler resolution for robust data inversion under different degrees of a priori information about the wind direction and the value of the characteristic Bragg frequency. Performance analysis and comparison with conventional ATI show that the proposed MB estimators can produce accurate velocity estimates in the absence of detailed ancillary data.

Maximum likelihood (ML) algorithms and Cramer-Rao (CR) bounds for the location and scale parameters of the Gumbel distribution are discussed. First we consider the case in which the scale parameter is known, obtaining the estimator of the location parameter by solving the likelihood equation and then evaluating its performance. We next consider the case where both the location parameter and the scale parameter are unknown and need to be estimated simultaneously from the reference samples. For this case, performance is analyzed by means of Monte-Carlo simulation and compared with the asymptotic CR bound.

Adaptive radar detection in non-Gaussian clutter is the subject of this work. The performance of two adaptive detection schemes developed in the literature, Kelly's generalized likelihood ratio test (GLRT) and the adaptive linear-quadratic (ALQ) detector, are tested on real sea clutter data recorded by the IPIX experimental radar (McMaster University, Canada) at the Osborne Head Gunnery Range (OHGR) in November 1993. The results of first- and second-order statistical analyses performed on two data sets are reported. Amplitude analysis has been carried out by checking the fitting to Weibull, log-normal, K, and generalized K models. The results show good agreement between performance prediction based on the generalized K model, with texture strongly correlated among primary and secondary data, and the performance obtained by processing the real sea clutter data.

The performance of ground-based surveillance radars strongly depends on the distribution and spectral characteristics of ground clutter. To design signal processing algorithms that exploit the knowledge of clutter characteristics, a preliminary statistical analysis of ground-clutter data is necessary. We report the results of a statistical analysis of X-band ground-clutter data from the MIT Lincoln Laboratory Phase One program. Data non-Gaussianity of the in-phase and quadrature components was revealed, first by means of histogram and moments analysis, and then by means of a Gaussianity test based on cumulants of order higher than the second; to this purpose parametric autoregressive (AR) modeling of the clutter process was developed. The test is computationally attractive and has constant false alarm rate (CFAR). Incoherent analysis has also been carried out by checking the fitting to Rayleigh, Weibull, log-normal, and K-distribution models. Finally, a new modified Kolmogorov-Smirnoff (KS) goodness-of-fit test is proposed; this modified test guarantees good fitting in the distribution tails, which is of fundamental importance for a correct design of CFAR processors.