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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.

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.

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.

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.

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.

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.

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 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.

In this chapter, cognition about the surrounding environment has been exploited to adapt the system to the interfering environment. Precisely, the robust joint optimization of the transmit signal and receive filter bank in the presence of signal-dependent interference has been considered. The Doppler shift of the moving target has been assumed unknown, and the worst case SINR at the output of the filter bank has been employed as figure of merit. By doing so, the effectiveness of the cognitive architecture has been investigated when the target Doppler frequency is a-priori unknown. In fact, while a rough Doppler knowledge is very reasonable during the detection confirmation or for an already tracked target, it is usually not available during the standard search radar operation and suitable cognitive algorithms are required.

Cognitive dynamic systems have been inspired by the unique neural computational capability of brain and the viewpoint that cognition (in particular the human one) is a supreme form of computation. Some exemplifications within this new class of systems, which is undoubtedly among the hallmarks of the twenty-first century, are cognitive radar, control, radio, and some other engineering dynamic architectures. Haykin published two pioneering articles in the context of the cognitive radar. The key idea behind this new paradigm is to mimic the human brain as well as that of other mammals with echolocation capabilities (bats, dolphins, whales, etc.). They continuously learn and react to stimulations from the surrounding environment according to four basic processes: perception-action cycle, memory, attention, and intelligence. This last observation highlights the importance of specifying which are the “equivalents” of the aforementioned activities in a cognitive radar.

A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, an efficient solution technique to handle the resulting constrained optimization problem is developed. Specifically, it is shown that the new family of distribution-free estimators shares a shrinkagetype form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. For the two most common norm instances, i.e., Frobenius and spectral, very efficient algorithms are developed to solve the aforementioned one-dimensional optimization leading to almost closed form covariance estimates. At the analysis stage, the performance of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) both for a spatial and a Doppler processing assuming different data statistical characterizations. The results show that interesting SINR improvements with respect to some counterparts available in the open literature can be achieved especially in training starved regimes., Comment: submitted for journal publication

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.

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.

This study is focused on the performance analysis of the sequential lobing technique for angle tracking in the presence of correlated fluctuating targets. The evaluation is conducted on the signal at the output of a conventional incoherent receiver followed by a logarithmic amplifier, assuming that the target returns at the two beam positions are correlated random variables. The general expression of the normalised root mean square error of the angular estimate is derived. Then, it is particularised to Swerling I target fluctuation at each of the beam positions. Additionally, assuming high signal-to-noise ratio, the probability density function of the angular estimate is provided. Finally, using Monte Carlo techniques, several numerical results are given and the effects of typical values of the correlation coefficient are discussed. © The Institution of Engineering and Technology 2013.

In this paper we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the Maximum Likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e. the sum of a positive semidefinite matrix plus a term proportional to the identity), and a condition number upper-bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of detection capability of an Adaptive Matched Filter (AMF) receiver with the proposed estimator in place of the sample covariance matrix, for a spatial processing. The results show that the AMF with the structured constrained covariance matrix estimator can achieve higher Detection Probabilities (PD), than some counterparts available in open literature. © 2012 IEEE.

This chapter is devoted to the presentation of some miscellanea application domains which could significantly benefit by the use of cognition. The first part is devoted to the presentation of the multiple-input-multiple-output (MIMO) radar transmit beampattern design process which can highly get advantage of the cognitive paradigm. It is well known that MIMO radar is a recently emerging paradigm enabling an enhanced performance over conventional radar in terms of target detection, identification, classification, and localization [1,2]. Additionally, colocated MIMO radar allows a higher flexibility in the transmit beampattern shape [3] based on the ability to transmit distinct waveforms via the probing antennas. This last feature is particularly attractive for cognitive radar systems, where the transmitter dynamically selects the best transmit beampattern in response to the receiver feedbacks, accounting for both previous experience/measurements and stored information. For instance, if the receiver detects in some angles strong unwanted returns, due to both clutter discrete and nonthreatening targets, the transmit beampattern can be shaped to exhibit small gain values in the mentioned directions so as to suppress the interference and to avoid overloading the processor with detections of no-tactical importance. Besides, multiple target tracking can be accomplished via multiple beams in the transmit beampattern (possibly adaptively interleaved with search beams) thus enhancing the multifunctionality of the system [4].

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.