The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase$\Phi$, the Faraday rotation$\Omega$, and the target orientation$\Psi$without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems.

The target coherency or covariance matrices are the main operators useful for characterizing the polarization transformation properties of radar target by modeling the depolarization effect. In this paper, a novel decomposition of the target coherency matrix is proposed, that is sufficient for representing the physical characteristics of the observed medium in term of a minimum set of orientation invariant parameters. The Einstein's photon circular polarization basis is used for obtaining an orientation invariant physical interpretation of the proposed parameter set both for deterministic and random target. A generalized unsupervised classification scheme is also proposed for underlining the effectiveness of the proposed decomposition theorem for classifying random reciprocal target into 75 physically meaningful clusters. The application of the proposed decomposition theorem and classification algorithm is useful for developing of novel Remote Sensing products and Data Mining softwares for monitoring the surfaces of the Earth and the Moon.

Passive radar systems exploit non-cooperative transmitter to detect targets in areas of interest. Some of the main advantages of such systems with respect to conventional radars include low cost architectures, low energy requirements and potentially null probability of intercept. In this paper a low-cost solution for vehicles detection making use of passive radar concept is presented. A Software Defined Radio (SDR) solution and commercial antennas have been used to realize a DVB-T passive radar demonstrator. An analysis of the DVB-T signal is firstly presented together with a study of its capability as radar waveform. Afterwards an experimental setup is presented and analysed and finally some results of targets detection are shown.

Sea Normalized RCS, and Doppler spectra have been revised for HF-OTH Clutter Modelling. The Hasselmann model is firstly introduced to predict the sea directional spectrum of fetch-limited sea and results have been compared with the Pierson-Moskovitz model used for large scale ocean remote sensing. Results show that the closed fetch-limited sea has lower NRCS compared with ocean for similar wind intensity and direction. For this reasons RCS and Doppler spectra must be predicted taking into account of the fetch dimension. In future work we will generalize this interesting approach to fetch-limited wind, time-limited pulse, in order to show the waveform effect on Doppler spectrum.

In hybrid SAR/ISAR systems full knowledge of the radar-target geometry is often not available. In such cases, both the cross-range scaling factor and the image plane are not known a-priori and they have to be estimated in order to classify the target. The modulus of the effective rotation vector can be estimated from the data and cross-range scaling can be achieved. Nevertheless, the orientation of such a vector, which is orthogonal to the image plane, can only be estimated in particular cases and from a long sequence of images. In this paper a novel technique, based on the use of bistatic radar imaging systems, for estimating the total rotation vector is proposed.

This paper introduces a new analysis technique, using the fractionally integrated autoregressive-moving average (FARIMA) model, to distinguish between low-wind and oil slick areas in high-resolution sea synthetic aperture radar (SAR) imagery. The method deals with the estimation of the fractional differencing and autoregressive-moving average parameters of the mean radial power spectral density of sea SAR images. The algorithm is applied and validated on dark areas corresponding to oil slicks, oil spills, and low-wind sea surface anomalies in European Remote Sensing 1 and 2 Precision Images of the Mediterranean Sea, North Sea, and Atlantic Ocean.

Over many years of research, several ISAR autofocusing techniques have been proposed. Today, we can divide them into two main categories: parametric and non-parametric techniques. The prominent point processing and the phase gradient algorithm are two classical examples of non-parametric techniques, whereas the more recent image contrast and entropy based techniques represent a new generation of parametric techniques. In this paper, the advantages and disadvantages of each technique are highlighted and a performance analysis is carried out by means of ISAR image reconstruction of real data.

The aim of This work is to define an analysis technique, which uses the fractionally integrated autoregressive-moving average (FARIMA) model, for discriminating wind falls from oil slick areas in sea SAR imagery. The method deals with the estimation of the fractional differencing and ARMA parameters for the sea SAR images mean radial PSD and is applied to some ERS-1 and ERS-2 images of the Mediterranean sea, North sea and Atlantic (Galicia) ocean containing only oil slick or only wind falls, or wind falls and oil slick anomalies.

This paper proposes a spectral analysis algorithm for sea SAR image fractal dimension estimation. The power spectral density of the sea SAR image is obtained through the periodogram approach and by decomposing the spectrum into sub-bands according to a dyadic scale. The fractal dimension of the image is obtained by a linear regression of the absolute maxima of each sub-band. This method can be applied globally or locally to have a single value or a map of the image fractal dimension. Examples on simulated and real data are reported.

Very high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets is a complicated task. In fact, the conventional range Doppler (RD) ISAR technique does not work properly when target motions generate terms higher than the first order in the phase of the received signal relative to each scatterer. This effect typically happens when at least one of these situations occur: (1) very high resolution images are required; (2) the target maneuvers; and (3) the target undergoes significant angular motions (roll, pitch, and yaw). A novel ISAR technique, named range instantaneous Doppler (RID), has been proposed for the reconstruction of very high resolution images of maneuvering targets. In this paper, we analytically show that the RID technique works properly when high-resolution ISAR images are required of maneuvering and/or rolling, pitching, and yawing targets; we also quantify the performance improvement of the RID technique with respect to the RD technique. The problem is tackled from an analytical point of view. First, we define a new model of the ISAR received signal that is valid for maneuvering targets, then we derive and compare the analytical expression of the point spread function (PSF) for the two techniques. Furthermore, we perform a statistical analysis to evaluate the improvement of the RID technique versus the RD technique in terms of spatial resolution. Finally, we prove the effectiveness of the RID technique by simulating the imaging process for two different targets: (1) a ship that undergoes roll, pitch and yaw motions and (2) a fast maneuvering airplane.

In this brief communication, some comments on the above paper are made. The efficiency of the method of analysis proposed by Vrana and other methods available in literature are compared.