This article addresses the design of bespoke adaptive detectors for point-like targets embedded in a sea-clutter-dominated environment using a multistatic/polarimetric radar network. The system consists of one monostatic as well as two colocated and cross-polarized bistatic sensors. The detector design accounts for possible range domain heterogeneity in sea-clutter backscattering, as well as potential functional relationships between the covariance matrices that characterize clutter returns across the bistatic polarimetric nodes. Accordingly, suitable estimates of the nuisance parameters for both monostatic and bistatic measurements are employed to develop adaptive decision rules based on the two-step generalized likelihood ratio test design criterion. The performance of the proposed receivers is assessed using simultaneously recorded monostatic and cross-polarized bistatic returns collected with the netted radar system. The analysis assesses both the constant false alarm rate (CFAR) behavior and the detection capability. The results show that despite minor deviations from ideal CFAR behavior when near-zero Doppler cells are tested, all the proposed decision rules maintain an overall robust CFAR behavior with respect to the nuisance parameters. In terms of detection capability, the proposed strategies outperform those relying solely on monostatic measurements and demonstrate comparable, or slightly improved, performance with respect to a competing approach confirming the effectiveness and robustness of the devised techniques.

This paper introduces and analyzes the concept of a cognitive inverse synthetic aperture radar (ISAR) ensuring spectral compatibility in crowded electromagnetic environments. In such a context, the proposed approach alternates between environmental perception, recognizing possible emitters in its frequency range, and an action stage, synthesizing and transmitting a tailored radar waveform to achieve the desired imaging task while guaranteeing spectral coexistence with overlaid emitters. The perception is carried out by a spectrum sensing module providing the true relevant spectral parameters of the sources in the environment. The action stage employs a tailored signal design process, synthesizing a radar waveform with bespoke spectral notches, enabling ISAR imaging over a wide spectral bandwidth without interfering with the other radio frequency (RF) sources. A key enabling requirement for the proposed application is the capability to successfully recover possible missing data in the frequency domain (induced by spectral notches) and in the slow-time dimension (enabling concurrent RF activities still in a cognitive fashion). This process is carried out by resorting to advanced methods based on either the compressed-sensing framework or a rank-minimization recovery strategy. The capabilities of the proposed system are assessed exploiting a dataset of drone measurements in the frequency band between 13 GHz and 15 GHz. Results highlight the effectiveness of the devised architecture to enable spectral compatibility while delivering high-quality ISAR images as well as additional RF activities.

Radio Frequency Interference (RFI) represents one of the major sources of performance degradation for Synthetic Aperture Radar (SAR) systems, especially for those operating at the microwave low-frequencies (e.g., L- and P-bands), where the spectrum sharing with other electromagnetic devices is increasingly common. In this work, pixel-wise RFI detection strategies operating on range-compressed (RC) SAR images are presented and applied to real-world airborne data acquired by the Italian Multiband Interferometric and Polarimetric SAR (MIPS) system. In particular, in the proposed approach, the RFI detection problem is recast in terms of the minimization of a Model Order Selection (MOS) rule, where the number of RFI-corrupted pixels is estimated through information theoretic paradigms, namely the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC). Experimental results demonstrate that the proposed procedure enables the effective suppression of the RFI-induced artifacts observed in the focused images while preserving the useful signal, thus confirming the suitability of the proposed approach for practical applications involving SAR data.

Identifying targets as belonging to uncrewed aerial vehicles or birds exploiting radar track sequences is critical for flight operation management and airport critical infrastructure safety. In this context, a hierarchical classification framework with "multifeature trustworthy integration (MFTI), multipreference decision fusion (MPDF), and multiwindow negotiation-based iterative reasoning (MWNIR)" is proposed. The MFTI employs a short-term multibranch deep evidential neural network, which leverages the Dirichlet process to reliably integrate the motion and radar cross section features of the target. The MPDF combines the outputs of two MFTIs with different preferences based on the Dempster-Shafer (DS) theory, providing reliable classification results on short-term track segments with different qualities. Finally, to reduce the impact of track data imprecision on the inference process, the MWNIR exploits a negotiation and iterative temporal DS-evidence fusion. Experiments on real-world radar track data demonstrate that the proposed algorithm can outperform some state-of-the-art methods in terms of classification accuracy and reliability.

This paper deals with the design of non-coherent jamming strategies capable of ensuring spectral compatibility with friendly radio frequency (RF) emitters. The goal is achieved via a cognitive approach, which, after recognizing the presence of friendly RF systems within the bandwidth of interest (perception), synthesizes a jamming waveform (action) with spectral notches, that allows to interfere exclusively with opposite emissions. Two methods are proposed for the synthesis of the jamming signal. The former leverages optimization techniques for quadratically constrained quadratic problems (QCQP) where each constraint embeds the interference level tolerable by a specific friendly RF system. The latter is a very computationally efficient approach based on simple projections, allowing a control over the spectral notch positions and widths. At the analysis stage, the performance of the devised jamming techniques is firstly numerically analyzed in terms of spectral occupancy and autocorrelation characteristics. The impact of the quantization process involved in the digital-to-analog conversion (DAC) of the jamming waveforms is also examined, with a particular focus on the spectral shaping impairments resulting from reduced DAC resolution. Finally, waveform transmission and reception is experimentally assessed with software defined radio (SDR) devices.

This paper deals with the design of slow-time coded waveforms which jointly optimize the detection probability and the measurements accuracy for track maintenance in the presence of colored Gaussian interference. The output signal-to-interference-plus-noise ratio (SINR) and Cramér Rao bounds (CRBs) on time delay and Doppler shift are used as figures of merit to accomplish reliable detection as well as accurate measurements. The transmitted code is subject to radar power budget requirements and a similarity constraint. To tackle the resulting non-convex multi-objective optimization problem, a polynomial-time algorithm that integrates scalarization and tensor-based relaxation methods is developed. The corresponding relaxed multi-linear problems are solved by means of the maximum block improvement (MBI) framework, where the optimal solution at each iteration is obtained in closed form. Numeral results demonstrate the trade-off between the detection and the estimation performance, along with the acceptable Doppler robustness achieved by the proposed algorithm.

This paper considers robust sparse beamforming (RSB) designs for a radar receive array via minimax and maximin signal-to-interference-plus-noise ratios (SINRs) criteria (assuming mismatches on the signal-of-interest steering vector and the interference-plus-noise covariance matrices) with a sparsity constraint on the beamvector. We first propose an approximation algorithm for the RSB solution for the minimax SINR problem, where a re-weighted $l_{1}$-norm regularization method is exploited to account for the sparsity requirement (by means of a tailored penalty term to the objective) and each problem of the underlying sequence of optimizations is recast into a semidefinite programming (SDP) problem by the strong duality theory. We show how to retrieve an optimal beamvector for the regularized minimax problem from the optimal solution to the SDP. For the RSB solution to the maximin SINR problem, an approximation algorithm is established similarly to the previous situation, but with the difference that the regularized maximin problem is transformed into a second-order cone programming problem. The latter approximation algorithm is computationally lighter than the former when the size of the array is sufficiently large. Simulation examples are presented to demonstrate the improved performance of the proposed two RSB solutions in terms of the normalized beampattern and array output SINR, compared to two existing non-robust sparse beamformers.

This paper discusses the design of adaptive detectors for point-like targets in a sea-clutter environment, using a radar system with one monostatic node and two co-located, crosspolarized bistatic sensors. The detector design considers spatial variation in sea-clutter backscattering and potential relationships between covariance matrices for the bistatic polarimetric channels. Therefore, appropriate estimates of the nuisance parameters for both monostatic and bistatic measurements are utilized to formulate adaptive decision rules based on the two-step Generalized Likelihood Ratio Test (GLRT) criterion. The performance of these receivers is evaluated using data from the NetRAD system analyzing their Constant False Alarm Rate (CFAR) behavior. Results show that, for all the synthesized receivers, the false alarm probability remains nearly constant with respect to the nuisance parameters, thereby confirming their ability to achieve the CFAR property.

This paper proposes a novel 2-D spectrum sensing method to improve environmental awareness capabilities compared with traditional grid-based techniques. Instead of using fixed angles of arrival (AOA), as dictated by a grid of points, the off-grid approaches allow for flexibility in estimating angle displacements at a reduced computational complexity increase. The recovery process is formulated as a regularized maximum likelihood (RML) estimation, leveraging block-sparsity. The optimization is tackled with a maximum block improvement (MBI) method to estimate noise power, the 2-D profile, and angular displacements. Moreover, two refinement strategies are used to improve the angle estimation accuracy. The method is validated through numerical simulations modeling realistic environments.

In low-altitude airspace, developing suitable techniques to distinguish between objects is essential for ensuring efficient and reliable surveillance. When using radar systems for surveillance, one of the most critical parameters characterizing a target is its Radar Cross Section (RCS). In fact, potentially hostile small drones typically exhibit a very small RCS (on the order of 0.01 square meters), posing challenges in distinguishing them from other non-hostile flying objects, such as birds. Therefore, gaining further insights into possible signatures in the RCS of drones is pivotal for developing bespoke detection strategies and suitable classification techniques. Therefore, the aim of this paper is to characterize the dynamic RCS of small flying drones by leveraging measurements collected via a Frequency Modulated Continuous Wave (FMCW) radar operating in the X-band, with a carrier frequency of 9.55 GHz. Several tests and measurements have been conducted using this system while two different sized drones, a DJI Matrice 300 and a DJI Mavic 2 Dual Enterprise, were flying within the radar surveillance area. At the analysis stage, The RCS fluctuations of each drone are analyzed by fitting the RCS data to various one- and two-parameter statistical models. The results reveal that different drones exhibit unique RCS fluctuation patterns, with each drone aligning with different statistical models that reflect their variations in size and structural complexity. These findings are crucial for developing tailored radar signal processing algorithms aimed at improving drone detection and tracking performance.

This two-part paper addresses maximally invariant detection of range-spread targets embedded in disturbance characterized by an unknown Kronecker product-structured covariance matrix. Part I focuses on Gaussian interference, whereas Part II extends the study to compound-Gaussian, clutter-dominated environments. Leveraging the principle of invariance, this part identifies a suitable transformation group that effectively compresses the nuisance parameter space, ensuring the constant false alarm rate (CFAR) property (with respect to the Kronecker-structured covariance matrix) for all invariant detectors. A maximal invariant and an induced maximal invariant are subsequently derived, serving as powerful tools to guide the design of CFAR detectors. Some existing two-step CFAR detectors for this structured situation are expressed as functions of the derived maximal invariant. Furthermore, two novel detectors (whose CFARity holds true under some mild technical conditions) are devised: the former employs a pseudo-missing strategy by treating elements possibly contaminated by target signals as missing and utilizes an Expectation-Maximization algorithm to perform the covariance matrix estimation; the latter is based on the one-step generalized likelihood ratio test criterion and is implemented via an alternate optimization algorithm. Finally, their CFAR behavior and detection performance are assessed through numerical examples, demonstrating their superiority with respect to some conventional decision rules.

In this paper we present a comparison between two recently proposed approaches for the detection of Radio Frequency Interference (RFI) corrupting Synthetic Aperture Radar (SAR) data. Specifically, we focus on a technique based on binary hypothesis tests [1], [2], which is able to provide one detection decision for each line of the radar samples acquired at a fixed azimuth position, and an approach involving the minimization of the Bayesian Information Criterion (BIC), which provides detection decisions on a pixel-by-pixel basis [3]. The main differences between these line-wise and pixel-wise methods are highlighted from both theoretical and experimental perspectives. In particular, the reconstruction results of simulated data corrupted with RFI, achievable through the considered detection approaches, are compared in terms of coherence and Normalized Mean Square Error (NMSE).

This paper addresses the synthesis of slow-time coded waveforms for single target tracking in a radar network operating under colored Gaussian interference. Based on the Posterior Cramér Rao Lower Bound (PCRLB), which characterizes the theoretically optimal accuracy of target state estimation, the problem at each tracking frame is formulated as the minimization of the trace of the PCRLB, together with power budget requirements and a similarity constraint to account for transmitter limitations and appropriate waveform features. To tackle this challenging optimization problem, an approximation solution technique is proposed, aimed at better tracking accuracy than the reference code. The resulting approximated problems, endowed with more tractable objective functions through Taylor-series expansion, are solved using a customized block Majorization-Minimization (block-MM) algorithm. The convergence properties of the developed procedure are thoroughly analyzed. Numerical results illustrate the accuracy improvements in the target state estimation process, and robust tracking performance under uncertain target state conditions achieved by the proposed technique.

This article introduces the Special Issue on Emerging Trends in Radar published in the IEEE Aerospace and Electronic Systems Magazine. The motivation and the immediate relevance for this special issue are described followed by a list of papers and a summary of each published article.

A polarimetric synthetic aperture radar (PolSAR) system, which uses multiple images acquired with different polarizations in both transmission and reception, has the potential to improve the description and interpretation of the observed scene. This is typically achieved by exploiting the polarimetric covariance or coherence matrix associated with each pixel, which is processed to meet a specific goal in Earth observation. This article presents a design framework for selecting the structure of the polarimetric covariance matrix that accurately reflects the symmetry associated with the analyzed pixels. The proposed methodology leverages both polarimetric and temporal information from multipass PolSAR images to enhance the retrieval of information from the acquired data. To accomplish this, it is assumed that the covariance matrix (of the overall acquired data) is given as the Kronecker product of the temporal and polarimetric covariances. An alternating maximization algorithm, known as the flip-flop method, is then developed to estimate both matrices while enforcing the symmetry constraint on the polarimetric covariance. Subsequently, the symmetry structure classification is formulated as a multiple hypothesis testing problem, which is solved using model order selection (MOS) techniques. The proposed approach is quantitatively assessed on simulated data, showing its advantages over its competitor, which does not exploit temporal correlations. For example, it reaches accuracies of 94.6% and 92.0% for the reflection and azimuth symmetry classes, respectively, while the competitor achieves 72.5% and 72.6% under the same simulation conditions. Moreover, the proposed method can realize a Cohen's kappa coefficient of 0.95, which significantly exceeds that of its counterpart equal to 0.78. Finally, the effectiveness of the proposed framework is further demonstrated using measured RADARSAT-2 data, corroborating the results obtained from the simulations. Specifically, tests conducted applying the Freeman-Durden Wishart classification have proved that the new approach greatly enhances the accuracy of pixel classification. For instance, in areas dominated by surface scattering, it boosts the percentage of correctly classified pixels from 68.23%, achieved using the classic method, to 91.65%.

In this article, we present a numerically efficient approach to optimally place wireless sensors for 3-D angle-of-arrival target localization. The proposed algorithm employs a block majorization-minimization method, in which each subproblem corresponding to a specific block involves a tailored surrogate of the objective function restriction. Unlike existing methods that focus solely on A-optimal design criteria, the proposed architecture can also be extended to handle the D-optimal design and exploits a bespoke parameterization in terms of trigonometric functions. This resulting algorithm has an iterative structure and provides, at each step a closed-form solution for sensors' azimuth and elevation angles, ensuring guaranteed convergence. Extensive numerical simulations are conducted to demonstrate the effectuality of the placement algorithm in scenarios involving uniform and nonuniform noise, as well as diverse receiver-target distances. The results indicate that this technique can outperform existing methods, particularly in terms of computational complexity, and appears well-suited for practical applications.

This article introduces a carefully designed experiment based on professional radar hardware setup to collect X- and quad-pol L-band radar data in a rural area during the simultaneous flight of a commercial and a professional-grade drone. After the definition of a procedure to retrieve the unmanned aerial vehicles (UAVs) radar cross section (RCS) from the gathered measurements, the research has focused on how the statistical behavior of the RCS depends on radar frequency, polarization configuration, and drone type. The analysis is based on the evaluation of the empirical cumulative distribution function (ECDF) of the normalized RCS and its comparison with different theoretical models whose goodness of fit is assessed using the Cram & eacute;r-von Mises (CVM) distance. In addition, dispersion metrics such as standard deviation (SD), interquartile range (IQR), full range (FR), and box-and-whisker plots are employed to provide a quantitative measure of the variability of the RCS under different operating conditions. The results show that, regardless of the frequency band, each UAV conforms to a distinct RCS fluctuation model. Moreover, for a given UAV, the best-fitting pattern changes depending on frequency and polarization. These variations reflect both the different structural characteristics of the UAVs and the frequency/polarization dependence of the RCS, as the scatterers that compose the drone may respond differently depending on the frequency/polarization.

Spectrum sensing is a key aspect of next-generation cognitive radars that make use of the perception-action cycle to improve their performance while endowing cohabitation with other systems. Awareness of the electromagnetic (EM) environment surrounding the radar is demanded to adapt its behavior to the changing scene. 2-D spectrum sensing is usually carried-out on uniformly spaced grids, over which the angle of arrival (AOA) of diverse (unknown) sources is estimated along with their frequency occupancy. To mitigate the performance degradations of on-grid methods, this article proposes an off-grid 2-D profile recovery strategy where the atoms are no longer fixed according to a given pool of nominal AOAs, but some flexibility is allowed to infer off-grid angle displacements. Hence, the angle-frequency profile recovery process is formalized as a regularized maximum likelihood estimation capable of exploiting the inherent block-sparsity of the overall profile. The resulting challenging optimization problem is handled through a maximum block improvement (MBI)-based method, which provides an estimate of the three variable blocks involved in the process, viz., noise power, 2-D profile, and angular displacements. Furthermore, in order to enhance the reliability of determining the space-frequency occupancy map and accurately estimating the angle displacements, three refinement strategies for the 2-D spectrum profile are suggested, suitably leveraging Bayesian information criterion and false discovery rate paradigms. The proposed framework is then validated through numerical simulations in some realistic EM environments, also comparing the three proposed refinement strategies.