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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.

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.

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.

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.

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.

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.