We present a sensor scheduling algorithm to plan the motion of multiple autonomous platforms for cooperative tracking of targets within a region that contains obstacles and occlusions. The platforms have kinematic constraints and their sensors have restricted field of view and range. The proposed algorithm is a variant of the Rapidly exploring Random Tree star algorithm (RRT*) that has been adapted to the problem of determining paths for multiple independent kinematically constrained platforms to optimise their tracking performance. To guide the scheduling algorithm, we define a tracking cost based on the Posterior Cramér Rao Bound (PCRB) derived from the predicted positions of the platforms and targets. Through simulations of generated paths, we show that the algorithm generates rational plans for tracking targets and that the tracking cost accurately predicts the realised performance of the platforms.

Most recently, deep learning (DL) has been used for inverse synthetic aperture radar (ISAR) imaging to overcome some shortcomings of the compressive sensing (CS)-based algorithms. Although DL-based ISAR imaging methods have effectively improved imaging quality, their performance remains sensitive to noise and sparse aperture. Moreover, many of these methods minimize the mean square error (MSE), which can result in overly smooth reconstructions and lose image details, such as weak scatterers. To solve these problems, an enhanced ISAR imaging method based on AU-Net is proposed in this letter. We design an AU-Net imaging network to improve the imaging accuracy of the model by introducing the attention mechanism to U-Net. Moreover, the combined loss function is designed by adding the$L1$and$L2$regularization terms, which further improves the recovery performance of the network for weak scatterers. Extensive simulations and experimental results validate the effectiveness and superiority of the proposed method compared to the existing algorithms.

The increasing reliance on convolutional neural networks (CNNs) for automatic target recognition (ATR) in critical applications necessitates robust defenses against adversarial attacks, which can undermine their reliability. To address this challenge, this letter proposes a novel classification framework that enhances CNN robustness for ATR under adversarial perturbations. Although CNNs are renowned for their high recognition accuracy, their performance can be compromised by subtle adversarial perturbations designed to deceive the classifier. Our methodology is based on extracting specific features from Shapley additive explanations (SHAP) analysis within and outside the detected target area. These features are then used to train a multinomial logistic regression model using the training labels, and the trained regressor performs the classification. The key strength of our framework relies on robustness enhancement against adversarial attacks, particularly designed by the fast gradient sign method (FGSM). We validate our findings through extensive evaluations using two publicly available datasets: the multitype aircraft remote sensing images (MTARSI) dataset, which contains optical images of various aircraft types, and the moving and stationary target acquisition and recognition (MSTAR) dataset, which contains radar images.

Single-channel synthetic aperture radar (SAR) can be applied to reconstruct the 3-D structure of maritime targets with the advantages of concise hardware design and lower data rate. Nevertheless, two problems should be solved to enable it, which are: 1) 2-D radar image focusing and 2) scatterer tracking. In this article, an integral 3-D reconstruction algorithm is proposed for solving these problems. First, the radar image is refocused by using the hybrid SAR and inverse SAR (ISAR) imaging technique. Second, a novel brightest scatterer (BS) extraction method is proposed combined with a clustering algorithm, which is effective and easy to implement. Third, a fast and robust scatterer tracking algorithm (STA) is presented to obtain the complete scatterer trajectories and to effectively enable the factorization method. Moreover, the proposed STA is applicable also in the presence of numerous scatterers and of scatterer scintillation. Afterward, the factorization method is employed to reconstruct the 3-D structure of the ship target. Finally, the effectiveness of the proposed algorithm is shown with the results of simulated and real measured data.

Autofocusing is an important step for inverse synthetic aperture radar (ISAR) imaging, while traditional autofocusing algorithms often fail in sparse aperture (SA) case. The residual phase error due to the imperfect motion compensation cannot be ignored for the compressive sensing (CS) based SA ISAR imaging. To tackle this issue, an autofocus-coupled approximate message passing with unitary transformation (AFUTAMP) algorithm for SA ISAR imaging is proposed in this paper. The phase error estimation process is introduced to the UTAMP framework for autofocusing, which enable UTAMP to simultaneously complete ISAR image reconstruction and autofocus, thereby leading to excellent performance of imaging and autofocusing. The effectiveness and superiority of the proposed algorithm are verified by the simulation results and experimental results based on measured data.

In the current literature, the linear cross-polarization term, denoted as $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $, is often considered a lost parameter in hybrid-polarimetry (hybrid-pol) SAR. This assumption is based on the belief that even with the reflection-symmetry condition (which holds for most natural surfaces), five parameters are needed to compute $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $, whereas hybrid-pol can only measure four parameters, leading to an underdetermined solution. We respectfully disagree with this viewpoint and assert that four parameters are indeed sufficient for accurately calculating $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $. Accordingly, we have derived a mathematical relationship under the reflection symmetry condition that provides a closed-form solution for calculating $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $ from hybrid-pol SAR datasets. This has been validated through both theoretical analysis and practical assessments using ALOS PALSAR L-band data

Automatic target recognition (ATR) is one of the most demanding applications of synthetic aperture radar (SAR) in the field of radar reconnaissance and surveillance. Convolutional neural networks (CNNs) have been extensively employed for SAR-ATR and obtained remarkable accuracy. However, regarding the black-box nature and non-transparency in decision-making, CNN’s reliability is unsatisfactory. Recently, some progress has been made toward providing a visual explanation of CNN’s classification procedure. In this paper, we employ the Local Interpretable Model-agnostic Explanation (LIME) algorithm to propose an interpretability metric that can be helpful to evaluate the overall robustness of CNNs. Using the proposed framework, the user can infer what proportion of the results are based on target features, while the remainder is based on irrelevant correlations from the background clutter. The theoretical findings are validated by the public MSTAR database.

In the application of oil-spill monitoring, the satellite revisit time needs to be as short as possible to identify minor spills before they can cause widespread damage. Simultaneously, it is required to capture a sufficient amount of information about the surface to clearly distinguish between oil-spilled and oil-free sea regions. The Hybrid-polarimetry (hybrid-pol) synthetic aperture radar (SAR) system can be exploited for such capabilities. However, limited hybrid-pol based oil-spill descriptors are reported in the literature in comparison to rich sets of full-polarimetry (full-pol) based descriptors. In this letter, we establish a direct relation between hybrid-pol data and full-pol data under reflection-symmetry condition. Consequently, through the proposed work, the rich sets of full-pol based oil-spill descriptors can be derived directly from the hybrid-pol datasets. For the validation of the proposed work, L-band ALOS PALSAR and UAVSAR datasets acquired over the Gulf of Mexico have been used.

In this work, a Polarimetric Interferometric In-verse Synthetic Aperture Radar (PolInISAR) based approach is outlined for the 3-D imaging of non-cooperative targets. The role of polarimetry is proven vital here as it allows to select the optimal scattering coefficients combination through which the highest coherence can be obtained. The highest coherence reduces any chance of uncertainties in the phase estimation. Consequently, with the accurate phase, the accurate 3-D image can be reconstructed under the form of 3-D cloud. For the validation of the proposed methodology, the real Tank-72 full-polarimetric ISAR dataset is implemented. The reconstructed result is found to be better superimposing with the original Tank-72 CAD model.

This paper introduces a polarimetric three-dimensional (3-D) interferometric inverse synthetic aperture radar (ISAR) imaging process using multiple phase-centers. This approach takes effective advantage of polarimetric scattering mechanisms in 3-D target representations, which may improve target classification and identification. A Pauli decomposition scheme is considered to study the role of polarimetry in 3-D Interferometric ISAR (InISAR). The polarimetric 3-D InISAR imaging process is validated using the backhoe synthetic data released by the Air Force Research Laboratory (AFRL).

This letter deals with inverse synthetic aperture radar (ISAR) autofocusing of noncooperative moving targets. The relative motion between the target and the sensor, which provides the angular diversity necessary for ISAR imagery, is also responsible for unwanted range migration and phase changes generating defocusing. In the case of noncooperative targets, the relative motion is unknown: the ISAR needs, hence, to implement an autofocus step [motion compensation (MoCo)] to achieve high resolution imaging. This task is typically carried out via the optimization of functionals based on general image quality parameters. In this letter, we propose the use of a fast and accurate MoCo algorithm based on the estimation of the Doppler parameters, thus fully coping with the nature of the imaging system. The effectiveness of the proposed method is proven on both simulated data and data acquired by operational systems.

The work addresses the problem of compensating the distortion effects induced by the translational motion of moving targets in Inverse Synthetic Aperture Radar (ISAR) imaging systems. The ISAR motion compensation is the most crucial step in the Autofocusing ISAR technique; this task is typically solved by implementing exhaustive search algorithms by adopting proper functionals based f.i. on image entropy or image contrast. In this work, we discuss an innovative and fast motion compensation procedure that is based on the estimation of two Doppler key Parameters: the Doppler Centroid and the Doppler Rate, which are related to the target motion parameters. The effectiveness of the proposed method is tested on real data acquired by a static Frequency Modulated Continuous Wave radar with an azimuth wide beamwidth; the radar is installed near the inner harbor of La Spezia (Italy) and it owned to the Centre for Maritime Research and Experimentation of the North Atlantic Treaty Organization (CMRE-NATO).

This paper addresses the problem of focusing moving targets in synthetic aperture radar (SAR) images. This task is solved here by using an inverse SAR (ISAR) technique. The ISAR technique performs an autofocus procedure by implementing exhaustive search algorithms, which are improved by classical convex optimization, of functions based on image contrast or entropy. In this paper, we discuss the possibility to perform an autofocus ISAR technique by exploiting the estimation of the target Doppler parameters, namely the Doppler centroid and the Doppler rate, which are related to the target motion parameters. The present algorithm is based on the reuse of efficient autofocus approaches that are classically used in direct SAR imaging. The effectiveness of the proposed method is tested on COSMO-SkyMed Spotlight SAR data of maritime targets. Furthermore, the proposed Doppler parameter estimation algorithm is compared with a well-known ISAR technique, namely the image-contrast-based technique.

The work addresses the problem of focusing moving target in very high resolution Synthetic Aperture Radar (SAR) images. This task is here solved by using an Inverse Synthetic Aperture Radar (ISAR) add-on approach. The ISAR technique performs an autofocus procedure by implementing exhaustive search algorithms of functions based on image entropy or contrast. The algorithm discussed in this work is based on the re-use of autofocusing technique classically adopted in SAR focusing. The effectiveness of the proposed method is tested on COSMO-Skymed Spotlight SAR data of maritime targets.

In this paper, we propose a multiframe marked point process model of line segments and point groups for automatic target structure extraction and tracking in inverse synthetic aperture radar (ISAR) image sequences. To deal with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on eight real ISAR image sequences of different carrier ships and airplane targets, using a test database containing 545 manually annotated frames.

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.

As recently demonstrated, passive radars are able to detect and track targets by exploiting illuminators of opportunity. In this paper, it will be proven that the same concept can be extended to passive inverse synthetic aperture radar (P-ISAR) imaging. A suitable type of signal processing is proposed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of passive-radar signal processing. Multiple-channel digital television broadcasting (DVB)-T signals are used to demonstrate the concept as they provide enough range resolution to form meaningful ISAR images. The problem of grating lobes, which are generated by the DVB-T signal, is also addressed and solved.

Advanced satellite SAR systems, such as Cosmo SkyMed, provide high resolution images with reasonably short revisiting time, allowing for a number of applications in areas such as Homeland Security and Maritime Surveillance. Especially in the case of maritime surveillance, non-ccoperative moving targets imaging represent a challenge for SAR systems as the complex target motions produce evident image defocusing. In the present paper, we show evidence of the effectiveness of the applications of ISAR processing to obtain well focused images of such type of targets. It will be also shown that super-resolution techniques are applicable to targets affected by complex motions after ISAR processing is used.

In this paper, the authors propose an innovative technique which is applied to the sea Synthetic Aperture Radar (SAR) imagery in order to aid the detection of the oil tankers responsible for oil spills. This technique aims to identify these oil spills and to correlate them to the detected ships. Real data acquired from COSMO-SkyMed (CSK) system have been processed to verify the effectiveness of this proposed new algorithm.

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.

In this paper we propose a Multi-frame Marked Point Process model for automatic target detection and tracking in Inverse Synthetic Aperture Radar (ISAR) image sequences. For purposes of dealing with high ISAR noise, we obtain the optimal target sequence by an energy minimization process, which simultaneously considers the observed image data and prior geometric interaction constraints between the target appearances in the consecutive frames. Finally, a robust permanent scatterer detection step is introduced to support the target identification process. Evaluation is performed on real ISAR image sequences of ship targets.

Reliable automatic target recognition (ATR) systems based on inverse synthetic aperture radar (ISAR) images require a robust feature selection. An ATR system based on polarimetric ISAR images has been recently proposed that extracts bright scatterers and uses their polarimetric signatures to define classification features. Since bright scatterers could be the results of multiple scattering, the concept of polarimetrically persistent scatterers (PPSs) has been introduced in a recent work. PPS is usually associated with single scattering mechanism and, therefore, may prove to be more robust for classification purposes. In this paper, an ATR system is defined that makes use of PPS. Furthermore, a detailed analysis is carried out to emphasize the meaning of PPSs when used for ATR.

In this paper, the problem of classifying nonhomogeneous man-made targets is investigated by performing a macroscopic and detailed target analysis. The Cloude-Pottier H/ αMLdecomposition is used as a starting point in order to find orientation-invariant feature vectors that are able to represent the average polarimetric structure of complex targets. A novel supervised classification scheme based on nearest neighbor decision rule is then designed, which makes use of the feature space. A validation process is performed by analyzing experimental data of simple targets collected in an anechoic chamber and airborne EMISAR images of eight ships. Three classification robustness performance indicators have been evaluated for each feature vector by performing the leaves-one-out-method described by Mitchell and Westerkamp. The robustness of the classifier has been tested with respect to the ability to reject unknown targets and to correctly identify known targets.

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.

ISAR imaging is a powerful signal processing that allows obtaining images of non-cooperative targets. Such images are often used as input to classification and recognition systems since they contain useful two-dimensional features. Nevertheless, the interpretation of ISAR images remains problematic since the image plane cannot be defined by the user but it depends on the target's own motions and on the relative position of it with respect to the radar. In this scenario, the only degree of freedom that is controlled by the user is the position of the sensor. In this paper, the problem of selecting an optimal position of the sensor to maximise the probability of obtaining a desired image projection plane is addressed. Moreover, mathematical tools are derived that may assist the user in deciding where to place an ISAR sensor given a priori knowledge of the scenario.

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.

One of the main benefits brought by the use of fully polarimetric radars is the ability to identify scattering mechanisms, which are related to the target physical properties. One of the most critical problems in synthetic range-profile reconstruction is the distortion induced by the target motion. Radial target velocity and acceleration generate second- and third-order phase terms in the received signal, which produce range shift and point-spread-function smearing. The distortions induced by the target motion produce, as a consequence, a signal-to-noise ratio loss. Recently, a method based on contrast maximization has been proposed in order to compensate target radial motions using single-polarization data. In this paper, an extension of such an algorithm is proposed that exploits fully polarimetric data in order to improve the target radial motion compensation.

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognizing targets. Moreover, the use of fully polarimetric ISAR (Pol-ISAR) images enhances classification capabilities. In this paper, the authors propose a novel automatic target recognition (ATR) technique based on the use of fully Pol-ISAR images and neural networks (NNs). In order to reduce the amount of data processed by the classifier, the brightest scattering centers are first extracted by means of the Pol-CLEAN technique, and then, their scattering matrices are decomposed using Cameron's decomposition. A classifier based on the use of multilayer perceptron NN that makes use of the features extracted from the Pol-ISAR images is then implemented. A proof-of-concept test is performed on real data acquired during a controlled experiment in an anechoic chamber.

Inverse Synthetic Aperture Radar (ISAR) images are often used for classifying and recognising targets. Moreover the use of a fully polarimentric ISAR image enhances classiication capabilities. In this paper, the authors propose a novel ATR technique based on the use of fully polarimetric ISAR images and Neural Networks. In order to reduce the amount of data processed by the classifier, the brightest scattering centres are first extracted by means of the Pol-CLEAN technique and then their scattering matrices are decomposed using Cameron's decomposition. The proposed ATR algorithm is finally tested on real data.

Scattering type classification represents a significant step toward target classification. Both the surface of Poincare's sphere and Cameron's unit disc have been used separately to represent symmetric scattering matrices and to define classification methods. In this letter, the equivalence of using the surface of Poincare's sphere and Cameron's unit disc in terms of characterization and classification of symmetric scattering types is demonstrated mathematically.

In stepped-frequency radar, target motions produce range-profile distortions. Range shift, signal-to-noise ratio loss, and symmetric spreading are produced by target radial velocity, whereas target radial acceleration is mainly responsible for asymmetric smearing. Acceleration-distortion effects are usually negligible when a high Pulse Repetition Frequency (PRF) is used, although this is not the case for low-PRF radars. In this paper, a new motion-compensation technique based on contrast optimization is proposed. The innovative contributions of this paper are as follows: (1) A theoretical analysis of the distortions produced by target motions on the reconstruction of synthetic aperture radar is provided; (2) the proposed technique compensates both phase terms, which are due to target radial velocity and acceleration; therefore, synthetic range profiles can be focused by processing low-PRF radar returns; (3) a new cost function for the synthetic range profiles (namely, contrast) is defined and used for motion compensation; (4) the proposed technique can be applied to any kind of stepped-frequency waveforms; and (5) an estimation error analysis is performed, first theoretically and then by means of both simulations and real data.

In stepped frequency radar, target motions produce range profile distortion. Specifically, the target radial velocity causes range profile shift, point spread function symmetric spreading and peak reduction, whereas the target radial acceleration is responsible for both asymmetric and symmetric point spread function spreading. This paper proposes a contrast- based technique for estimating the target motion parameters and therefore for reducing range profile distortions.

Cameron's coherent target decomposition and classification is able to represent a symmetric scatterer onto a unit disc in the complex plane, and assign it to one of the six symmetrical elemental scatterer classes. Recently, Touzi et al. proposed a variation of Cameron's method by introducing a coherent analysis. Moreover the Poincare's sphere, was used instead of the unit disc for representing symmetric scattering because it was considered a more suitable domain. The aim of this work is to demonstrate the equivalence of using Poincare's sphere domain and Cameron's unit disc, in term of characterisation and classification of symmetric scattering types.

In the past decade the use of fully polarimetric SAR (polSAR) systems has increased significantly due to their effectiveness in target classification and detection applications. While polSAR imagery has been extensively used to distinguish between different scattering mechanisms in a scene, there has been a lack of research in the exploitation of polarimetry to assist in image formation and in particular autofocus for fine resolution SAR. In this paper an extension of the phase gradient algorithm (PGA) for polSAR imaging is proposed and its effectiveness is tested on simulated and real data.

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yanget al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yanget al. is shown.

In ISAR systems, fully polarimetric capabilities have not been fully exploited for target classification or recognition. In this paper, a full system that reconstructs the polarimetric ISAR image and classifies the target is proposed and tested on simulated data.

This paper proposes a novel algorithm for estimating the fractal dimension of sea synthetic aperture radar (SAR) images. The algorithm is based on the variation method, and it is suitably designed for the analysis of sea SAR images. The SAR image fractal dimension is a feature that provides a measure of the image roughness. Such a feature can play an important role in the classification process for recognizing the presence of anomalies on the sea surface. The innovation aspects of this paper are listed as follows: (1) an extension of the variation method, which was proposed for the fractal analysis of one-dimensional signals, to the case of two-dimensional (2-D) functions; (2) a numerical formulation of the variation method, which is suitable for processing 2-D discrete signals; and (3) an optimization of the algorithm for sea SAR image analysis. The algorithm is tested and validated both on simulated and real ERS-1/2 Precision Image sea SAR images and compared with the classical estimation algorithm based on spectral analysis

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.

In this paper we consider the phase degradation performance between an ideal radar path and that of a sea surface reflected signal (over a variety of sea surface heights) in an Emulated Bistatic Radar scenario. The sea surface model employed is a ID Fractal model that is based on the Generalised Weierstrass Function. Graphical and Tabular results are provided that demonstrate the absolute performance using the simulation technique discussed, for sea heights up to 0.9m.

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.

SAR images from space-borne platforms have proved to be helpful data for identification of oil spills and other surface anomalies, such as low wind areas, man-made targets, and natural films. The use of fractal dimension, which is related to the concept of surface "roughness", as a feature for classification, improves the detection of anomalies, since enhances texture discrimination. In the particular case of oil slicks, the surface tension of seawater is increased and the surface wave motion is significantly depressed. This effect relatively reduces the sea surface roughness, decreases the radar backscattered energy and enables oil slicks to be discernible from the radar image. Several algorithms may be applied for local fractal dimension estimation, but most solutions are tailored for specific applications and are characterized by estimation accuracies depending on the adopted image model and also on the value being estimated. This paper describes a decision-based fusion approach for local fractal dimension estimation of SAR images of the sea surface. Three different estimation algorithms are considered and the three resulting fractal maps are fused by means of a weighted average. The weights are calculated from the performance characteristics of the three algorithms measured on synthetic fractal surfaces. The experimental results carried out on ERS-2 SAR images prove the effectiveness of the proposed decision-based fusion approach

The aim of this paper is to investigate whether fractal maps extracted from sea SAR images are useful for discriminating the sea from other entities or anomalies. Fractal mapping consists of locally estimating the fractal dimension of the image. To this purpose four different methods based on covering and spectral analysis are proposed and compared when applied to real ERS1-2 GEC images. Wind falls, sea and line coast are well distinguishable in the fractal maps. This result clearly shows that the use of image fractal processing is a promising and powerful technique for identifying sea surface anomalies.

In this paper the authors analyze the correlations between sea surface roughness, wind intensity and radar backscattering time series, by using the fractal geometry. The aim is to provide evidence, by using real data, of correlation between the sea surface roughness and the backscattering fractal dimension. The results have highlighted good correlations and shown the usefulness of the fractal dimension as a parameter for sea surface roughness characterization.

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.