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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.

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.

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 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.

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.

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 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.

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

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.

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.

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.

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, it is shown that the new class of distribution-free estimators shares a shrinkage-type form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. At the analysis stage, the effectiveness of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) also in comparison with some existing counterparts. © 2017 IEEE.

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.

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

This paper deals with detection of oil spills from multipolarization synthetic aperture radar images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the polarimetric covariance matrix (PCM) equality (absence of oil spills in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the backscattering associated with the oil spills leads to a signal, in some eigendirections, weaker than the one gathered from a reference area where the absence of any oil slicks is a priori known. A multifamily generalized likelihood ratio test approach is pursued to come up with an adaptive detector ensuring the constant false alarm rate property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but nonimplementable) structure and some other suboptimum adaptive detectors available in the open literature. This study, which is conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach.

The authors consider the problem of covariance matrix estimation in heterogeneous environments for radar signal processing applications, where the secondary data exhibit heterogeneity in local power and share the same covariance structure. Without resorting to the complete statistical characterisation of the sample support, a class of estimators, each of them defined as the geometric barycenter of a set of basic covariances estimates (obtained from the available secondary data) with a specific distance employed, is proposed. The basic estimates are obtained by exploiting the characteristics of positive-definite matrix space and a condition number upper bound constraint. Finally, they evaluate the detection capabilities of an adaptive normalised matched filter with the proposed estimators in the presence of compound-Gaussian disturbance comparing it with existing alternatives.

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.

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.

This paper deals with detection from multipolarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of target in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the target leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is apriori known the absence of targets. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant False Alarm Rate (CFAR) property. At the analysis stage, the behaviour of the new architecture is investigated in comparison with a benchmark (but non-implementable) and some other adaptive sub-optimum detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach. © 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.

This letter deals with the problem of adaptive signal detection in partially-homogeneous and persymmetric Gaussian disturbance within the framework of invariance theory. First, a suitable group of transformations leaving the problem invariant is introduced and the Maximal Invariant Statistic (MIS) is derived. Then, it is shown that the (Two-step) Generalized-Likelihood Ratio test, Rao and Wald tests can be all expressed in terms of the MIS, thus proving that they all ensure a Constant False-Alarm Rate (CFAR)., Comment: submitted for journal publication

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.

Phase modulation induced by target micro-motions introduces side-bands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that is capable of uniquely describing the micro-motion. 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. Specifically, the proposed algorithm consists in the extraction of the pseudo-Zernike moments from the Cadence Velocity Diagram (CVD). 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. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes.

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.

We deal with performance prediction of the incoherent radar receiver (i.e. squared-law plus integrator) in the presence of independent non-identically distributed (non-id) Weibull target echoes. First of all, we provide the probability density function (pdf) for the sum of independent but non-id Weibull random variables in terms of an infinite sum of Gamma pdfs. Then, we develop an analytic expression for the detection probability as a fast converging series of functions. Finally, we evaluate the impacts on the detection performance of non-id Weibull target parameters. © 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 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.

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.

In this paper we deal with the problem of detecting extended targets embedded in Gaussian interference with structured covariance matrix. We model the target echo from each range bin as a deterministic signal with an unknown scaling factor that accounts for the target response. We also exploit some a-priori knowledge about the operating environment at the design stage. Specifically, we assume that inverse disturbance covariance matrix belongs to a set described through a family of unitary invariant convex functions. Hence, we derive a class of Generalized Likelihood Ratio Tests (GLRT's) for the resulting hypothesis test. At the analysis stage, we assess the performance of some detectors, lying in the aforementioned class, in terms of Detection Probability (PD). The results highlight that the better the covariance uncertainty characterization, the better the detection performance. © 2013 IEEE.

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 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.

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 semi-definite 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 achievable signal-to-interference-plus-noise ratio (SINR) both for a spatial and a Doppler processing. The results show that interesting SINR improvements, with respect to some existing covariance matrix estimation techniques, can be achieved. © 2012 IEEE.