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In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.

This paper deals with the problem of covariance matrix estimation for radar signal processing applications. We propose and analyze a class of estimators which do not require any knowledge about the probability distribution of the sample support and exploit the characteristics of the positive definite matrix space. Any estimator of the class is associated with a suitable distance in the considered space and is defined as the geometric barycenter of some basic covariance matrix estimates obtained from the available secondary data set. Then, we introduce an adaptive detection structure, exploiting the new covariance matrix estimators, based on two stages. The former consists of a data selector screening among the training data whereas the latter is a conventional Adaptive Matched Filter (AMF) taking the final decision about the target presence. At the analysis stage, we assess the performance of the proposed two-stage scheme in terms of probability of correct outliers excision and target detection. The analysis is conducted both on simulated data and on the challenging KASSPER datacube. © 2012 EUROPEAN MICROWAVE ASSOC.

The availability of accurate estimates of the delay or time of arrival (TOA) of the incoming signals is of paramount importance for the position estimation in passive radars with multiple receivers. This correspondence aims at improving estimation of the delays by multiple detectors exploiting the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The resulting equation system is formulated as a least squares (LS) minimization problem, whose solution is efficiently found computing the pseudo-inverse of the model matrix. In fact, the cross-cross-correlation implicitly performs a filtering operation on the considered signal, approximating the generalized cross-correlator behavior, without using statistical information about the signal spectra. The proposed method is numerically validated in comparison with classic counterparts and theoretical bounds.

In this letter, an automatic method to validate the reciprocity theorem on full-polarimetric heterogeneous synthetic aperture radar (SAR) data is derived. The study extends, to the more general heterogeneous scenario, the work of [1], where the conformity with the reciprocity is studied in the homogeneous case. At the design stage, it is assumed that the pixels in the polarimetric image share the same covariance structure but different power levels. Then, the dependence on nuisance parameters is removed resorting to the Principle of Invariance. The resulting problem is formalized as a binary hypothesis test and is solved through the generalized likelihood ratio test (GLRT). Tests are conducted both on simulated and real-recorded data to show the superiority of the proposed GLRT with respect to its homogeneous counterpart.

This letter proposes a novel spectrum sensing method for cognitive satellite communications for detecting primary user signals. First, the useful signal power is estimated from the noisy received signal by combining the second- and fourth-order moments of data under investigation. Second, the possible presence of the signal of interest is stated by exploiting the aforementioned useful power as the decision variable. Computer simulations, substantiated by theoretical results, are carried out to evaluate the performance of the method in comparison with recently published techniques. The results evidence the efficiency of the presented technique for cognitive satellite communications.

This paper tests a SAR image coregistration method, developed to account for a joint rotation and range/azimuth shift effect in absence of zooming, on foliage penetrating (FOPEN) data. In particular, the method is referred as a constrained Least Squares (CLS) optimization method and, in its basic form, it sharply extracts all patches composing the entire image. Differently, in next developments it applies a detection stage to identify extended areas in the images where patches are then selected. Moreover, it also performs a refinement of the equations in the CLS problem through an iterative cancellation procedure. The performance of this enhanced version of the CLS are made on the challenging Carabas-II VHF-band FOPEN SAR data to demonstrate its effectiveness also in high-resolution SAR images.

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling; it derives the optimum phase-only STAP, namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the $N$ -torus of phase-only complex vectors, where $N$ is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phase-only STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.

This paper focuses on the problem of parameter estimation for a Fluctuating Two-Ray (FTR) model in the context of wireless mobile communications. Precisely, the received signal is assumed to be the superposition of two dominant components (typically a direct plus a reflected path signal) in addition to diffusive secondary contributions. The signal components may be affected by random amplitude shadowing, statistically modeled by Nakagami- $m$ distribution, multiplied by unknown scaling factors with random uniform independent phases, whereas the diffusive component is assumed to follow the complex Gaussian distribution. Exploiting the method of moments, a $4\times 4$ nonlinear system is herein mathematically derived, which is very hard to be solved due to the strong nonlinearity. Therefore, a sequential procedure based on some prior information about the diffusive component power level is devised to solve it. The effectiveness of the proposed estimation technique is shown by evaluating the normalized root mean square errors as well as mean errors and standard deviations in several operating conditions of practical interest also considering the limit case of only one-ray in order to compare the proposed approach to simpler estimators, already presented in the literature. The results show the robustness of the new estimator even under a multipath model mismatch. Finally, the effectiveness of the proposed estimation procedure is confirmed through measured mmWave data.

Non-cooperative target location is accomplished by means of multiple passive radar receivers deployed in the region of interest and that detect the delayed replicas of the signal emitted by the target and estimate the time difference of arrival. However, in realistic scenarios, some of the involved sensors could not correctly work if the sensor is victim of intentional/unintentional interference and/or physical damage of the device or its communication link. Thus, procedures for failure detection become of primary interest to discard the measurements related to the out-of-order sensors. The approach proposed in this paper identifies sensors under failure from the analysis of the errors in the equationsystem implemented to estimate the delays. More precisely, we first compute the second and fourth order correlations (namely, cross-correlation and cross-cross-correlation, i.e. the cross-correlation between signals' cross-correlations) of the incoming signals to build up the system of equation. Then, we perform a sequential cancellation of the equationsthat experience the highest errors. A statistical test based on the number of canceled equationsrelated to a specific sensor is used to state whether or not the specific sensor is under failure. Finally, the performance of the entire failure detection architecture is assessed by numerical simulations also in comparison with a heuristic method based on the percentages of canceled equationsand its standard counterparts not performing any outlier screening.

This letter extends the constrained Least Squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS.

This paper proposes a convolution neural network (CNN) architecture for automatic recognition of signals on the basis of their modulations for Cognitive software defined radio (SDR) applications. It is developed starting from two CNNs specifically designed for this problem and is characterized by having a low number of convolution and fully connected layers sharing also a very low number of filters/units. Moreover, Batch normalization is used to increase learning rate and reduce training time. The reduced complexity together with its low operating time make it compliant with real-time SDR applications. The proposed architecture is validated on the RadioML2016.10a dataset showing interesting results in discriminating both analog and digital modulations under different signal to noise ratio (SNR) regimes.

Recently, multi-sensor localization strategies are gaining attention in the railway scenario. In fact, the current trend is to reduce or remove the physical equipment deployed along the track for positioning purposes and to exploit on-board sensors to realize the same functionalities. Although GNSS is one of the major resources to perform this task, its performances dramatically decrease in presence of sources of local hazards like multipath, shadowing and blockage. For this reason, multi-sensor positioning methods are under study. Among them, those based on the detection of landmarks constituted by georeferenced trackside infrastructure elements like rail signs, and the estimation of the relative position of the train with respect to them are rather promising. Thus, in this paper we focus on the construction of the section of a Rail Digital Map related to these infrastructure elements on the basis of the fusion of the outputs of a stereo video camera and a LIDAR. In particular, the algorithms for object detection, single epoch landmark position estimation and landmark tracking are discussed. Results of the performance assessment based on Monte Carlo simulations are also reported.

One of the major challenges in the design of high accuracy, high integrity localization procedures for rail applications based on Global Navigation Satellite Systems is represented by the local hazards that cannot be mitigated by resorting to augmentation networks. By fact, combining smoothed code pseudoranges with (differential) carrier phase and/or with Inertial Measurement Unit's outputs is ineffective against multipath low frequency components. These issues can be mitigated by processing images, depth maps and/or pointclouds provided by imaging sensors placed on board. The absolute position of the train can be determined by combining its relative position with respect to georeferenced rail infrastructure elements (e.g., panels, signals, signal gantries) provided by the visual localization processing unit with the landmark absolute position. In addition, the visual input can be exploited for determining on which track the train is located and can be used as complementary odometry source. Moreover, the information provided by the visual localization processing unit can be used to monitor integrity and compute the protection levels. In this contribution we present a localization system that integrates a Global Navigation Satellite System receiver, Inertial Measurement Units, and video sensors (such as monocular and stereo video camera, Time of Flight camera and LIDAR), and has the potential to overcome some of the operational and economical limitations of the current train localization system employed in the European Railway Traffic Management System.

Maximum likelihood (ML) estimation of the loading factor under affine constraints on the covariance eigenvalues is addressed. Several situations of practical interest for radar are considered, and the corresponding ML solutions to the loading factor estimation problem are derived in closed form. Furthermore, it is shown that the constrained ML problem, the constrained geometric approach, and the constrained problem of mean square error minimization (with respect to the loading factor) all lead to the same solution. At the analysis stage, the effectiveness of the resulting covariance estimators is evaluated in terms of both the signal-to-interference-plus-noise ratio and the receiving beampattern shape and compared with that of other covariance estimation methods available in the literature. Finally, a receiving architecture based on the adaptive matched filter that exploits the new loaded covariance estimators is also considered to assess the benefits of the new strategies in terms of detection probability.

This letter proposes a coregistration algorithm to compensate for the possible inaccuracy of trajectory sensor during the synthetic aperture radar (SAR) image acquisition process. Such a misalignment can be modeled as a pure displacement in range and azimuth directions and a rotation effect due to different angles of sight. The approach is formalized as a constrained least squares (CLS) optimization problem enforcing a constraint of the absence of a zooming effect between the two SAR images. Moreover, the system equations can optionally be weighted according to local properties between the extracted patches within the quoted couple. Interestingly, the solution can be obtained in closed form, therefore, with a low computational cost. The results of the tests conducted on the 9.6-GHz Gotcha SAR data demonstrate the capability of the strategy to properly register the imagery.

This letter considers the design of a two-stage direction of arrival (DOA) scheme for radar systems. Precisely, at the first stage a sparse recovery approach is used to obtain both DOA and complex amplitude estimates of the incoming signal. Since the DOA is evaluated on a predefined grid of bins sampling the antenna azimuth mainbeam, at the second stage, a closed-form complex-valued parabolic interpolation is performed to refine it. By doing so, the angle accuracy is improved, but at the same time maintaining fixed the overall computational complexity. Numerical results show the enhancement provided by the proposed procedure to the initial sparse recovery method.

The SARS-CoV-2 is quickly spreading worldwide resulting in millions of infection and death cases. As a consequence, it is increasingly important to diagnose the presence of COVID-19 infection regardless of the technique applied. To this end, this work deals with the problem of COVID-19 classification using Computed Tomography (CT) images. Precisely, a new feature-based approach is proposed by exploiting axial CT lung acquisitions in order to differentiate COVID-19 versus healthy Computed Tomography (CT) images. In particular, first-order statistical measures as well as numerical quantities extracted from the autocorrelation function are investigated with the aim to provide an efficient classification process ensuring satisfactory performance results.

Entropy concept is related to uncertainty and predictability of random time series. The estimated trend of such a parameter can provide useful information and possibly predict future behavior of a number of non-stationary noisy signals. The goal of this paper consists of analyzing the Covid19 signal made by the number of registered infections in Italy during the first four months of the pandemic epidemy (March-June 2020). Finally, some considerations are drawn after matching historical dates of some Covid-19 related Acts made by the Italian Government (i.e., lockdown and unlockdowns). Based on the obtained results, we could conjecture that the provisions have inducted people to a common behavior concerning local mobility during the lockdowns and the progressive unlockdowns of the quarantine period in Italy.

This paper deals with the problem of loaded/unloaded drones classification. Precisely, exploiting the different micro-Doppler signatures exhibited by a drone with both any load and payloads of different weights, a novel signature extraction procedure is developed for automatic recognition purposes. The developed algorithms is based on a novel adaptation of the spectral kurtosis technique to the problem at hand, specifically the analysis of narrowband and wideband spectrograms of the radar echoes reflected by the drones. In addition, the principal component analysis is used to reduce the feature vector size. The experiments conducted on measured bistatic radar data prove the effectiveness of the proposed method in separating the quoted classes of objects.

This paper devises a tunable detection architecture to deal with mismatched signals embedded in Gaussian interference with unknown covariance matrix based on a sparse recovery technique. Specifically, a sparse learning method is exploited to estimate the amplitude and angle of arrival of the possible targets, which are then employed to design detectors relying on the two-stage detection paradigm. Remarkably, the new decision scheme exhibits a bounded-constant false alarm rate property. The performance assessment, carried out through Monte Carlo simulations, shows that the new detectors can outperform classic counterparts in terms of rejecting mismatched signals, while retaining reasonable detection performance for matched signals.

This paper considers the problem of censoring outliers from the secondary dataset in a radar scenario where the sample support is limited. To this end, the generalized regularized likelihood function (GRLF) criterion is used and the corresponding regularized maximum likelihood (RML) estimate of the outlier subset is derived. Since the exact RML estimate involves the solution of a combinatorial optimization problem, a reduced complexity but approximate RML (ARML) procedure is also designed. As to the selection of the regularization parameter, both the expected likelihood (EL) principle and the cross-validation (CV) technique are exploited. At the analysis stage, the performance of the RML and ARML procedure is evaluated based on simulated data in comparison with some previously proposed methods. The results highlight that the RML and ARML algorithm achieves, in general, a satisfactory performance level whereas the previously proposed techniques often experience some performance degradation when the volume of training data is dramatically limited.

This paper considers the design of tunable decision schemes capable of rejecting with high probability mismatched signals embedded in Gaussian interference with unknown covariance matrix. To this end, a sparse recovery technique is exploited to enhance the resolution at which the target angle of arrival is estimated with the objective to obtain high-selective detectors. The outcomes of this estimation procedure are used to devise detection architectures relying on either the twostage design paradigm or heuristic design procedures based upon the generalized likelihood ratio test. Remarkably, the new decision rules exhibit a bounded-constant false alarm rate property and allow for a tradeoff between the matched detection performance and the rejection of undesired signals by tuning a design parameter. At the analysis stage, the performance of the newly proposed detectors is assessed also in comparison with existing selective competitors. The results show that the new detectors can outperform the considered counterparts in terms of rejection of unwanted signals, while retaining reasonable detection performance of matched signals., 13 pages, 13 figures

The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to minimize the number of shoots per kill. For this reason, a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper, a model and a robust framework is developed, which incorporates different micro-Doppler-based classification techniques. The reliability of the proposed framework is tested on both simulated and real data.

The challenge of Automatic Target Recognition (ATR) of military targets within a Synthetic Aperture Radar (SAR) scene is addressed in this paper. The proposed approach exploits the discrete defined Krawtchouk moments, that are able to represent a detected extended target with few features, allowing its characterization. The proposed algorithm provides robust performance for target recognition, identification and characterization, with high reliability in presence of noise and reduced sensitivity to discretization errors. The effectiveness of the proposed approach is demonstrated using the MSTAR dataset.

In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies.

This letter studies the conformity with the reciprocity theorem on the measured polarimetric synthetic aperture radar (SAR) data. The problem is formalized via a binary hypothesis test where the reciprocity assumption is tested versus its alternative (absence of reciprocity). The generalized likelihood ratio (GLR) is used as design criterion and the resulting decision rule ensures the constant false alarm rate (CFAR) property. At the analysis stage, the performance of the GLR statistic is analyzed on the simulated data as well as on two different measured data sets (collected by two systems) thus highlighting the effectiveness of the approach.

This paper describes a new framework that, exploiting the Kullback-Leibler Divergence, allows to address the design of one-stage adaptive detectors for multiple hypothesis testing problems. Precisely, at the design stage, the problem is formulated in terms of multiple alternative hypotheses competing with the null hypothesis. Then, a one-stage decision scheme is derived in the context of both known model and unknown parameters as well as for the most general case of unknown model and parameters. Interestingly, the resulting detectors are given by the sum of the compressed log-likelihood ratio based on the available data and a penalty term depending on the number of unknown parameters. This general architecture is then particularized to the problem of subspace target detection, and its effectiveness is assessed through simulations also in comparison with its counterpart based on the two-stage paradigm.

This paper is focused on noncooperative target position estimation via the joint use of two-dimensional (2-D) hyperbolic and elliptic passive location techniques based on time difference of arrival (TDOA) and passive coherent locator (PCL) measurements, respectively. A fusion strategy is laid down at the signal processing level to obtain a reliable estimate of the current target position. With reference to the scenario with a single transmitter of opportunity, the mathematical model for joint exploitation of TDOA and PCL strategies is formulated. Then, the Cramer-Rao lower bound (CRLB) for the Cartesian coordinates of the target is established and the theoretical performance gains achievable over the localization technique using only TDOA or PCL observations are assessed. Finally, TDOA-PCL hybrid 2-D localization algorithms are provided and their performance in terms of root-mean-square error is compared with the square root of the CRLB.

A new algorithm for Passive Bistatic Radar (PBR) localization exploiting multiple illuminators of opportunity is proposed. To capitalize a-priori information on the receiving antenna main-lobe extent, specific constraints are forced to the target localization process. At the estimator design process the elliptic positioning problem is formulated according to the constrained Least Squares (LS) framework. Hence, the resulting non-convex optimization problem is globally solved providing a closed-form estimate to the target Cartesian coordinates. At the analysis level, the performance of the new estimator is assessed in terms of Root Mean Square Error (RMSE) behavior. The results highlight that interesting MSE improvements with respect to some counterparts available in the open literature can be achieved especially at low Signal to Noise Ratio (SNR) values.

Magnetic resonance (MR) images can play a very important role to evaluate patients’ diagnosis. In particular, there is an increasing interest in image processing and advanced texture analysis methods able to extract features from MR images that are not easily to percept by the human eye. Among many, Haralick’s features have been strongly exploited referring to texture analysis of medical images. Therefore, in this paper, we have investigated Haralick’s features computed from MR T2-weighted acquisitions in order to differentiate benign to malignant salivary gland tumors. The study has involved a total of 6 patients affected by salivary gland cancer: from the followup exams performed by radiologists, 3 patients have been identified as benign tumor affected while 3 patients as malignant one. Haralick’s textural features are computed from normalized gray level co-occurrence matrix (GLCM) considering four different spatial relationships. In this preliminary study all the 14 Haralick’s textural features are investigated in our attempt to differentiate benign from malignant salivary gland tumors: the obtained results reveal that these textural features may be useful to point out the differences between the tumor’s nature, helping the clinicians with the diagnosis routine of the disease.

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection, and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, the coherence matrix, and the Muller matrix and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric synthetic aperture radar (SAR) images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image.

The problem of high range resolution profile (HRRP) estimation is considered in this paper. In particular, stepped frequency waveforms are devised to enhance the target range profile (RP) estimation accuracy. The basic idea relies on the dynamic optimization of the probing waveform accounting for some feedback information to minimize the profile estimation error. The transmitted frequency hopping pattern is selected so as to minimize the predicted Cramer–Rao lower bound (CRLB) associated with the amplitudes and locations of the scattering centers falling in the coarse range bin under test (i.e., the target profile). Specifically, it is assumed that the target imaging is initially performed via a conventional linear stepped frequency transmission, hence, based on the collected data, an initial prediction of the target profile is derived (perception). Then, the RP estimation is enhanced progressively according to the cognitive paradigm, via a specific frequency pattern selection at the next transmission (action). The results highlight the capabilities of the cognitive approach to provide interesting benefits with respect to the classic linear stepped frequency strategy.

This letter proposes a novel technique for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Focusing on the heterogeneous scenario wherein the polarimetric image pixels share the same covariance but different power levels, the original data are replaced by a maximal invariant statistic in order to remove the dependence on the scaling factors. Then, the classification problem is formulated as a multiple hypothesis test which is addressed by applying the model order selection rules. The performance analysis is conducted on both simulated and measured data and points out the effectiveness of the proposed approach.

In this study, authors address high-range-resolution (HRR) profile reconstruction, when stepped-frequency waveforms are eventually used to maintain a narrow instantaneous bandwidth, resorting to the sparse learning via iterative minimisation (SLIM) paradigm, a regularised minimisation approach with an l(q)-norm constraint (for 0 < q

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations.

In this paper, an automatic classification approach for polarimetric covariance structure is derived and assessed. It extends the framework of Pallotta et al. “Detecting Covariance Symmetries in Polarimetric SAR Images” to the heterogeneous environment, where the pixels of the polarimetric image share the same covariance structure but different power levels. The Principle of Invariance is exploited to replace the original data with a suitable statistic whose distribution is independent of the scale factors. Then, the classification problem is formulated in terms of a multiple hypotheses test and solved by means of model order selection rules. The behavior of the newly devised classifiers is first assessed over simulated data also in comparison with the analogous counterparts for a homogeneous environment. Next, the classification performances are evaluated on real measured data corroborating the satisfactory results highlighted in the simulations.

In this paper, we describe novel techniques for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Specifically, we investigate two operating scenarios. In the first scenario, it is assumed that the polarimetric image pixels locally share the same covariance (homogeneous environment), whereas the second scenario considers polarimetric pixels with different power levels and the same covariance structure (heterogeneous environment). In the second case, we invoke the Principle of Invariance to get rid of the dependence on the power levels. For both scenarios, we formulate the classification problem in terms of multiple hypothesis tests which is addressed by applying the model-order selection rules. The performance analysis is conducted on both simulated and measured data and demonstrates the effectiveness of the proposed approach. © 2019 IEEE.

This paper deals with adaptive radar detection of a point-like target in a homogeneous environment characterized by the presence of clutter, jamming, and radar internal noise. At the design stage, two training datasets, whose gathering is carefully motivated in the paper, are considered to get receiver adaptation. Hence, the maximum likelihood estimator of the interference covariance matrix for the cell under test is computed exploiting both the available secondary sets. This estimate is then used to build two adaptive decision rules based on the two-step generalized likelihood ratio test and Rao test criteria. Remarkably, they are not limited by the conventional constraint on the cardinality of the classic training dataset. At the analysis stage, the detection performances of the newly proposed detectors are compared with those of the analogous conventional counterparts and the interplay among the different parameters of the problem is thoroughly studied.

The problem of High Resolution Range Profile (HRRP) estimation is considered in this paper. In particular, stepped frequency waveforms are devised to enhance target Range Profile (RP) estimation accuracy. The basic idea relies on the dynamic optimization of the probing waveform accounting for some feedback information to minimize the profile estimation error. The results highlight the capabilities of the cognitive approach to provide significant benefits with respect to the classic linear stepped frequency strategy. © 2017 IEEE.