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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.

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.

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.

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.

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 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.

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.

This chapter has been devoted to the design of a tracker exploiting cognition at multiple levels. Specifically, environmental maps and characteristics of the targets, available in the dynamic database possibly learned from the feedback channel, have been used to gain improved tracking performance in a multiple targets scenario exploiting measurements provided by a tracking radar. Unlike the conventional tracking radar (which is very sensitive to false alarms and/or missed detections), the main advantage of the cognitive paradigm is the significant reduction in the number of false alarms, missed detections, false tracks, and improved true target track life. In the second part of the chapter, the focus has been on waveform selection to optimize the target tracking process. Specifically, it has been assumed that a waveform library is available at the transmitter, and the most suitable signal (in the sense of minimizing the predicted tracking estimation error) is chosen for the next dwell. The proposed algorithm is based on the use of feedback information from the receiver and exploits a standard KF. The performance of the proposed strategy has been studied in a challenging scenario accounting for a maneuvering target in the presence of thermal noise only or RF interference plus thermal noise. The results have highlighted that the adaptive feedback process guiding the waveform selection is able to provide advantages over the classic radar tracker, which does not resort to transmit adaptivity.

Radar performance is strongly dependent on the transmitted waveform and its parameters which must be adapted to the surrounding environment, radar mission, goal, and task. Waveform diversity is a relatively new paradigm involving a continuous variation and adaptation of the transmitted signal to dynamically optimize the radar performance and fulfill the more and more stressing requirements. In this context, cognitive radar waveform design in spectrally dense environments is a very challenging and topical problem. This paper deals with the synthesis of signals optimizing radar capabilities while satisfying spectral compatibility constraints. Specifically, the design of radar waveforms, sharing appealing features and ensuring spectral coexistence with other Radio Frequency (RF) systems, is introduced and discussed according to a rigorous framework.

Cognitive radar is a new paradigm to conceive the next radar generation characterized by unique and amazing features inspired to mental abilities and processes related to knowledge. It goes beyond the usual extraction of information from the measurements and requires the radar to get intelligence. Introduced by Haykin [1] and Guerci [2], it is attracting huge attention within the radar community during the last few years. The key concept is that radar system performance can be enhanced through a continuous and coordinated feedback between the transmitter and receiver which implies a dynamic adaptation of the sensor algorithms to the operational context and environmental replies. This paper discusses the biological inspiring principles of cognitive radar and describes the resulting conceptual architecture. Then, a radar signal processing application, which can significantly benefit from cognition, is illustrated highlighting the potential performance improvements achievable with the awesome pro-active paradigm. © 2016 IEEE.

The radio frequency (RF) electromagnetic spectrum is a limited natural resource necessary for an ever-growing number of services and systems. It is used in several applications, such as mobile communications, radio and television broadcasting, as well as remote sensing. Together with oil and water, the RF spectrum now represents one of the most important, significant, crucial, and critical commodities due to the huge impact of radio services on society. Both high-quality/high-rate wireless services (4G and 5G) as well as accurate and reliable remote-sensing capabilities (air traffic control (ATC), Earth geophysical monitoring, defense and security applications) call for increased amounts of bandwidth [1], [2]. Besides, basic electromagnetic considerations, such as good foliage penetration [3], low path loss attenuation, and reduced sizes of the devices push some systems to coexist in the same frequency band [4] (for instance VHF and UHF). As a result, the RF spectrum congestion problem has been attracting the interest of many scientists and engineers during the last few years and is currently becoming one of the hot topics in both regulation and research fields [5], [6]. © 2016 IEEE.

The challenge of ballistic missiles classification is getting greater importance in last years. In fact, since the antimissile defence systems have generally a limited number of interceptors, it is important to distinguish between warheads and confusing objects that the missile releases during its flight, in order to maximize the interception success ratio. For this aim, a novel micro-Doppler based classification technique is presented in this paper characterized by the employment of Krawtchouk moments. Since the evaluation of the latter requires a low computational time, the proposed approach is suitable for real time applications. Finally, a comparison with the 2-dimensional Gabor filter based approach is described by testing both the techniques on real radar data. © 2016 IEEE.

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from synthetic aperture radar platforms. The author's approach exploits both channel (e.g. polarisation) and spatial diversity to obtain suitable information for such a critical task. In particular they use the pseudo-Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.

Reliable micro-Doppler signature classification requires the use of robust features describing uniquely the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper the application of the pseudo-Zernike moments for micro-Doppler classification is introduced demonstrating the effectiveness of the proposed approach by classifying real data. The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. © 2014 IEEE.

In the modern battlefield scenario multiple sources of information may be exploited to mitigate uncertainty. Polarization and spatial diversity can provide useful information for specific and critical tasks such as the Automatic Target Recognition (ATR). In this paper the use of pseudo-Zernike moments applied to the full-polarimetric Gotcha dataset is presented. Specifically improved single platform ATR performance is demonstrated through the use of multiple observations.

This study deals with the problem of covariance matrix estimation for radar signal processing applications. The authors propose and analyse a class of estimators that 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, the authors 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 taking the final decision about the target presence. At the analysis stage, the authors assess the performance of the proposed two-stage scheme in terms of probability of correct outliers excision, constant false alarm rate behaviour and detection probability. The analysis is conducted both on simulated data and on the challenging KASSPER datacube. © The Institution of Engineering and Technology 2013.

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. © 2012 IEEE.

A high-contrast inpainting scheme based on the Complex Ginzburg-Landau equation recently applied successfully to image restoration is applied to SAR interferograms to improve their quality and therefore final quality of Digital Elevation Models (DEMs). The new technique attempts to recover the phase values in low coherence regions through diffusion and inpainting. After phase unwrapping low coherence regions are masked and discarded and a Complex Ginzburg-Landau (CGL) inpainting scheme is applied to regions where phase values are missing. We demonstrate that the residues reduce and the proposed algorithm leads to a higher Signal-to-Noise Ratio (SNR) if compared with MCF algorithm. The restoration technique has been applied to ERS-1 and ERS-2 data sets acquired on July 1995. Results appear to be very promising: the proposed algorithm provides good performances especially in presence of strong noise level and low coherence areas with relatively small dimensions. © 2010 IEEE.

This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels of major classification steps, namely classification, recognition and fingerprinting, demonstrating the effectiveness of the proposed approach to discriminate drones from birds, fixed wings from multi-rotors and drones carrying different payloads on real measured radar data.