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This article addresses adaptive radar detection of $N$ pulses coherently backscattered by a prospective target in heterogeneous disturbance. As customary $K \geq N$ range cells adjacent to the one under test are used for estimation purposes. The disturbance in each range cell is described by a non-Gaussian model based on a mixture of $L < K$ Gaussian distributions. Gaussian components are characterized by an unknown low-rank matrix plus thermal noise with unknown power level. We first derive a detector inspired by the generalized likelihood ratio test that adaptively estimates the statistical properties of the disturbance from the observed data. To overcome the intractability of the involved maximum-likelihood estimation problem, a suitable approximate strategy based on the expectation-maximization algorithm is developed. This also allows us to classify the cell under test by selecting the “maximum a posteriori Gaussian distribution” for the disturbance (under both hypotheses). Accordingly, a likelihood ratio test is also proposed. An extensive performance analysis, conducted on synthetic data as well as on two different experimental datasets (PhaseOne and IPIX for land and sea radar returns, respectively), shows that the proposed approaches outperform state-of-the-art competitors in terms of both detection capabilities and false alarms control.

In this paper, we address the problem of detecting a Noise-Like Jammer (NLJ) that does not quickly transmit all the available power but it gradually increases the transmitted power. This control strategy would prevent conventional electronic counter-countermeasures from revealing the presence of a noise power discontinuity in the window under test. As a consequence, the radar system under attack becomes blind requiring a reaction by an expert operator. In order to face such a situation, we devise two innovative NLJ detection architectures by assuming at the design stage specific models for the NLJ power variation. The first model is based on a linear variation law over the observation time, whereas in the second model, the NLJ power experiences unconstrained fluctuations in the window under test. Under these hypotheses, we resort to ad hoc modifications of the generalized likelihood ratio test, where the unknown parameters are replaced by suitable estimates obtained through iterative procedures. The performance analysis, carried out using synthetic data, shows the effectiveness and superiority of the proposed detectors over the conventional approach.

Multiple-input-multiple-output (MIMO) system with frequency diverse array (FDA) offers interesting perspectives for target detection and estimation in several fields, such as sonar. In this context, reverberation in shallow water environments is highly heterogeneous and, as a consequence, impairs the performance of methods that assume statistical homogeneity. To deal with this drawback, we propose a weighted sparse algorithm to enhance the performance of FDA-MIMO sonars in the context of space–time adaptive processing (STAP). Specifically, we exploit the structure of the FDA-MIMO sonar signal model to construct an optimization problem that allows for high-resolution estimation of the angle–Doppler spectrum. In order to solve such a problem, we assume that the weight vector is sparse and conceive a sparse STAP method for the related estimation. In addition, we also estimate a regularization parameter that controls the sparsity level of data by using the constant-false-alarm-rate (CFAR) detector and the maximum likelihood (ML) estimator. As a consequence, the negative effect related to the amount of training data or accurate prior knowledge of the reverberation statistics is no longer present. Both target parameter estimation accuracy and reverberation suppression performance are superior over the conventional sparse-STAP and sparse direct data domain methods. Simulation results show the effectiveness of the proposed method.

This letter presents a fast gridless direction-of-arrival (DOA) estimation algorithm that improves the Fast Interior-Point Method (FastIPM). The proposed algorithm effectively achieves a significant reduction in computational load for large-scale arrays while maintaining an accurate estimation. It reduces the complexity of gridless DOA estimation to $\mathcal {O}(N^{2})$ per iteration (according to the Landau notation). Compared to the original FastIPM, we extend the received signal data model to account for more general scenarios that include missing array elements and arbitrary number of snapshots. By formulating the problem as an optimization with an obstacle function, we iteratively minimize the dual gap to obtain the optimal solution for atomic norm minimization problem. Extensive numerical simulations demonstrate the algorithm's computational superiority over the state-of-the-art gridless DOA estimation methods, providing excellent resolution and accuracy.

In this paper, we consider the joint detection and localization (in the delay-Doppler domain) of targets by using Multiple-Input Multiple-Output radars. Specifically, inspired by the fact that most of existing contributions do not encompass target energy spillover between adjacent matched filter samples, we firstly analyze all the energy configurations associated with the target position within the delay-Doppler cell under test. Then, at the design stage, we formulate the detection problem by considering all data samples that might contain target components. The resulting problem is solved by applying the generalized likelihood ratio test (along with the two-step modification) when possible or ad hoc approximations of it dictated by the mathematical intractability of specific energy configurations. In these last cases, we devise two estimation procedures grounded on either a cyclic optimization of the likelihood function or the minimization of a suitable upper bound. Remarkably, the resulting architectures are capable of estimating the target position within the cell under test in the delay-Doppler domain. Finally, the behavior of the proposed architectures in terms of detection and localization performance is assessed over synthetic data and in comparison with conventional detectors that do not consider the spillover of target energy. The examples clearly show the superiority of these new detectors over the considered competitors as well as their capability of returning reliable estimates of target delay-Doppler position.

In this article, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple-hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model-order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors toward more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.

This article addresses the challenge of identifying the polarimetric covariance matrix (PCM) structures associated with a polarimetric synthetic aperture radar (SAR) image. Interestingly, such information can be used, for instance, to improve the scene interpretation or to enhance the performance of (possibly PCM-based) segmentation algorithms as well as other kinds of methods. To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only one unknown structure is assumed for data belonging to a two-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different PCM structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation–maximization algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms.

The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community. In this paper, we provide a solution to this problem by devising detection architectures capable of classifying the range bins according to their clutter properties and detecting possible multiple targets whose positions and number are unknown. Remarkably, the information provided by the proposed architectures makes the system aware of the surrounding environment and can be exploited to enhance the entire detection and estimation performance of the system. At the design stage, we assume three different signal models and apply the latent variable model in conjunction with estimation procedures based on the expectation-maximization algorithm. In addition, for some models, the maximization step cannot be computed in closed-form (at least to the best of authors' knowledge) and, hence, suitable approximations are pursued, whereas, in other cases, the maximization is exact. The performance of the proposed architectures is assessed over synthetic data and shows that they can be effective in heterogeneous scenarios providing an initial snapshot of the radar operating scenario.

This article deals with the detection of range and Doppler distributed targets imbedded in non-Gaussian clutter. The clutter is modeled as a spherically invariant random process with unknown texture components and a covariance matrix structure. We also assume a set of secondary signal-free data is available to estimate the correlation properties of the clutter. Moreover, the target signal at each range cell is assumed to be a sum of returns from an unknown number of scattering centers (SCs) with unknown amplitudes and Doppler frequencies. A generalized likelihood ratio test based on adaptive Doppler steering matrix estimation is proposed in this work. The detector assumes that the target SCs over the range bins are sparse and exploits a sparse Bayesian optimization model to estimate the unknown Doppler steering matrix. In addition, an adaptive iterative algorithm is proposed to solve the estimation problem. The performance assessment conducted by Monte Carlo simulation confirms the robustness and effectiveness of the proposed detector.

Modern wireless communication networks are threatened by new generations of radio hackers. These are skilled attackers equipped with low-cost software radios, suitably instrumented so as to monitor, degrade, or even alter the radio signals. The aim of this paper is to devise innovative detection architectures against the most common classes of threats: broadband noise jammers, whose goal is to reduce the signal-to-noise ratio, and spoofing/meaconing attacks, which aim to inject false or incorrect information into the receiver. To this end, we resort to the hypothesis testing theory and solve the associated problems by means of the GLRT possibly accounting for penalty terms. The resulting decision schemes represent the main technical novelty of this work. The analysis of their performance focuses on a location security case study for 4G/5G cellular networks. To this end, we leverage measurement models from the cellular localization literature and generate data according to these models. The numerical results show the effectiveness of the proposed approaches in comparison with suitable counterparts.

Noncooperative 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 article identifies sensors under failure from the analysis of the errors in the equation system implemented to estimate the delays. More precisely, we first compute the second- and fourth-order correlations (namely, cross-correlation (CC) and cross-cross-correlation, i.e., the CC between signals' CCs) of the incoming signals to build up the system of equation. Then, we perform a sequential cancellation of the equations that experience the highest errors. A statistical test based on the number of canceled equations related 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 equations and its standard counterparts not performing any outlier screening.

Modern radars face challenging scenarios characterized by high levels of heterogeneity that make conventional detection/estimation algorithms no longer effective since most of them rely on the well-known homogeneous environment. In this article, we propose innovative classification schemes capable of identifying the radar operating scenario in terms of clutter properties. To this end, different situations are accounted for at the design stage ranging from the homogeneous environment to the simultaneous presence of clutter edges and clutter discretes/outliers. Then, we conceive decision rules based upon the so-called penalized log-likelihood ratio test that exploits the inherent sparse nature of the observed scene. In this context, two different sparsity-promoting priors are used to model the behavior of clutter discretes/outliers and the unknown parameters are estimated by means of cyclic procedures. The performance analysis, conducted on both simulated and real-recorded data, highlights the effectiveness of the proposed classifiers at least for the considered operating parameter values. Remarkably, radar systems can take advantage of such classification results by selecting homogeneous data that can yield reliable estimates (namely, that are functions of homogeneous data) of the unknown interference parameters, and hence enhanced detection performance with respect to the case where unclassified heterogeneous data are used.

Hyperspectral target detection is a task of primary importance in remote sensing since it allows for location and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with other counterparts by showing the effectiveness of the proposed procedure.

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts.

In this letter, we address the problem of channel classification. Specifically, we consider five channel models also accounting for the incoming fifth generation mobile communication network (5G). The classification problem is formulated in terms of a multiple hypothesis testing problem and solved through a heuristic decision logic relying on sequential binary comparisons. This choice is dictated by the mathematical intractability of probability density functions (PDFs) under some hypotheses which contain transcendental functions. As a consequence, decision rules raising from well-known design criteria based upon data distribution might suffer numerical instability related to the routines used to implement such PDFs. The performance analysis is conducted on simulated data and shows the effectiveness of the proposed strategy in channel classification.

The received signal in Rician multipath channels is modeled as a complex constant, accounting for the main line-of-sight (LoS) path, added to a circular complex Gaussian random variable, modeling all the diffusive secondary paths. The Rician $K$ -factor is accordingly defined as the ratio between the powers of LoS and diffusive components. Under this (widely assumed) multipath model, a closed-form expression for the Rician $K$ -factor estimate is commonly obtained exploiting the second and fourth moments of the received signal amplitude. However, in mobile communications, random shadowing effects make the LoS component fluctuating (Rician shadowed model) leading to a performance degradation of the conventional estimator. With focus on land mobile satellite communications, in this letter, a novel estimation procedure of the Rician $K$ -factor, aimed at avoiding the bias error of the conventional estimator when the LoS component is Nakagami- ${m}$ distributed, is proposed and assessed. Specifically, the estimate is computed using the solution of a system of three equations and three unknowns in conjunction with the likelihood function of the received signal. The performance analysis shows that the new estimator can guarantee better performance than the conventional estimator.