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 paper 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 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%.

The problem of the identification of Radio Frequency Interferences (RFI) that corrupt Synthetic Aperture Radar (SAR) data is addressed in this work.In particular, we propose a new approach based on the minimization of the Bayesian Information Criterion (BIC) able to detect, on a pixel-by-pixel basis, the RFI signals within the considered SAR data.The performance of the proposed approach is assessed by means of both simulated and real-world airborne L-band SAR data corrupted by RFI signals.

The design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. This is of paramount importance to develop an analytic model for the received signal samples, which is mandatory for the synthesis of radar detectors. In this respect, the aim of this paper is the development of suitable learning tools to study some important statistical features of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. Precisely, stationarity of the data in the slow-time domain is first assessed resorting to Generalized Inner Product (GIP) based statistics. Then, the possible presence of structural symmetries in the clutter covariance matrices is investigated. Finally, relationships between some statistical parameters characterizing the sea-clutter returns on the bistatic polarimetric channels are explored via a specific sequential hypothesis testing. This research activity is complemented by the use of radar returns measured via the Netted RADar (NetRAD), which collects simultaneously monostatic and bistatic polarimetric measurements. The results indicate that the analyzed data can be modeled as drawn from a stationary Gaussian process within the coherence time. Additionally, the bistatic returns on the different polarimetric channels can be assumed statistically independent with speckle components possibly exhibiting proportional/equal covariance matrices depending on the transmit/receive polarization and bistatic geometry.

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced, and two iterative algorithms are proposed for the optimization of the equivalent statistical learning framework. Both strategies are based on the Majorization Minimization (MM) paradigm and hence enjoy nice properties such as monotonicity and ensured convergence to a stationary point of the equivalent MLE problem. The proposed framework is also extended to deal with MLE of other practically relevant covariance structures, namely, the banded Toeplitz, block Toeplitz, and Toeplitz-block-Toeplitz. Through numerical simulations, it is shown that the new methods provide excellent performance levels in terms of both mean square estimation error (which is very close to the benchmark Cramér-Rao Bound (CRB)) and signal-to-interference-plus-noise ratio, especially in comparison with state-of-the art strategies. Moreover, the estimation task is accomplished with a remarkable reduction in computational complexity compared with a standard approach relying on a Semidefinite Programming (SDP) solver.

It is widely recognized that SAR images can suffer from the presence of RFI, especially at the low frequencies of the microwave spectrum. Such nuisance signals can significantly impair the quality of the SAR data with unwanted effects on the final products. This paper describes a processing strategy for the detection and cancellation of RFI signals corrupting airborne SAR raw-data. In addition, the performance of the proposed strategy is assessed by exploiting airborne L-band FMCW-SAR data.

This article deals with the synthesis and analysis of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms to mitigate the impairments of ambiguous returns in quadrature-polarimetric (quad-pol) synthetic aperture radars (SARs). To this end, focusing on signals with a continuous piecewise linear instantaneous frequency, the design of a waveform pair exhibiting both a low cross correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. To handle the resulting nondeterministic polynomial (NP) hard problem, a coordinate descent (CD) method is employed, where, at each step, the marginal minimization is tackled via a MATLAB optimization toolbox. Hence, transmission/reception schemes jointly capitalizing quasi-orthogonal NLFM waveforms and azimuth phase coding (APC) techniques are proposed to suppress ambiguity interference. Moreover, a systematic framework for the evaluation of the resulting azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) is provided. Finally, detailed simulation experiments based on the LuTan (LT-1) parameters are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

This article outlines the archetype of a novel SpaceBorne Radar (SBR) in the Ka-band for space situational awareness (SSA) based on a code-division multiplexing (CDM) multiple-input–multiple-output (MIMO) payload transceiver. Considering small-size hypervelocity debris, the functional architecture of the fully polarimetric SBR is described, including key comparisons with previous works based on single-input–multiple-output (SIMO) configurations. SBR operations are clarified via timing hierarchies in surveillance mode, the complex data hypercube structure, and the low pulse repetition frequency (L-PRF) range and range rate search (RRRS) entailing a track while simultaneous search (TWSS) contacts collection strategy. Ancillary details on the SBR functional architecture provide paramount insights to ponder critical MIMO aspects and pave the way for key research and development efforts. Finally, numerical results provide a proof of concept for the signal processor upstream the constant false alarm rate-like (CFAR-like) detection block.

This article deals with the effects of plasma turbulence on the detection performance of spaceborne radars (SBRs) for space situational awareness (SSA). Physical insights on both channel and target phenomenology lead to reasonable statistical models with a focus on the fading occurrence probability (FOP) in case of weak scintillation. Consequently, the performance analysis of conventional radar detectors in additive white Gaussian noise (AWGN) is provided in a monostatic configuration for either Rayleigh or Rice fluctuating targets, and considering Rice plasma scintillation as a function of the scintillation index$s_{4}$. Numerical results identify a paramount framework to characterize the influence of plasma turbulence on SBR detection performance for SSA. Finally, ancillary notes make provision for tailoring the performance analysis also in the case of bistatic radar configurations.

This article delves into a novel spaceborne radar payload transceiver for augmenting space-based monitoring capabilities for near-Earth space situational awareness. The sensor complex data hypercube structure and sensor timing hierarchies in surveillance mode are addressed along with a suitable low pulse repetition frequency range and range-rate search with a pause while scan collection strategy. A bespoke active electronically scanned array-based Pulse Doppler Radar sensor in the Ka-band is then defined in order to cope with the significant Doppler stress characterizing a burst of echoes from hypervelocity debris targets. Before feeding the onboard Bayesian tracker, the complex data hypercube is processed by means of a Doppler filter bank including pulse compression in cascade with a constant false alarm rate-like block.

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.

Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra' projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.

This letter deals with the robust joint design of radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case Signal-to-Interference-plus-Noise-Ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique in terms of detection performance, spectral shape and autocorrelation features.

This letter deals with similarity parameter selection for knowledge-aided covariance matrix estimation in adaptive radar signal processing. Starting from the observation that the maximum likelihood estimate of the interference covariance matrix under a similarity constraint admits a closed-form expression, which depends on the similarity parameter, an adaptive procedure is devised to get a parameter free estimator. The technique is based on the expected likelihood principle and requires the solution of an implicit equation, which can be efficiently pursued via the bisection method due a monotonicity property. The analysis of the estimator, conducted also in comparison with the counterpart based on the cross-validation method confirms its effectiveness in terms of both performance and computational complexity.

Adaptive detection of a signal competing with Gaussian interference with spatially varying power is addressed in this letter. Invariance principle is used to focus the attention on a class of decision rules exhibiting some specific symmetries, which represent a sufficient condition to ensure the constant false alarm rate property. The class of invariant decision rules is characterized via the design of a maximal invariant statistic together with its equivalent in the parameter space. Finally, it is proved that the adaptive normalized matched filter using a suitable recursive estimate of the interference covariance structure coincides with a maximal invariant component.

In this paper, an automatic classification approach for polarimetric covariance structure is derived and assessed. It extends the framework of Pallotta et al. “Detecting Covariance Symmetries in Polarimetric SAR Images” to the heterogeneous environment, where the pixels of the polarimetric image share the same covariance structure but different power levels. The Principle of Invariance is exploited to replace the original data with a suitable statistic whose distribution is independent of the scale factors. Then, the classification problem is formulated in terms of a multiple hypotheses test and solved by means of model order selection rules. The behavior of the newly devised classifiers is first assessed over simulated data also in comparison with the analogous counterparts for a homogeneous environment. Next, the classification performances are evaluated on real measured data corroborating the satisfactory results highlighted in the simulations.

A new derivation of the Capon spectral estimator is provided in this letter enriching the set of its possible interpretations. Specifically, it is shown that it defines the best rank-one approximation (according to any distance measure induced by a unitary invariant norm) of the data covariance matrix along the dyadic product specified by the useful signal direction. Remarkably, the Capon power estimate is obtained as solution to a convex optimization problem, which represents the starting point toward the development of a new class of robust estimators.

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 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 paper, we address the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. To this end, we exploit the spectral properties of the clutter to transfer the binary hypothesis test problem from complex domain to real domain. Then, we devise and assess a detection strategy based on the so-called two-step Generalized Likelihood Ratio Test (GLRT) design procedure. Finally, a preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterparts which ignore the spectrum symmetry.

Radar signal design in spectrally dense environments is a very challenging and topical problem. This letter deals with the synthesis of waveforms optimizing radar performance while satisfying multiple spectral compatibility constraints. Unlike some counterparts available in the open literature, a specific control on the interference energy radiated on each shared bandwidth is enforced. To tackle the resulting NP-hard optimization problem, a polynomial computational complexity procedure based on semidefinite relaxation (SDR) and randomization is developed. Hence, some numerical results are shown to highlight the effectiveness of the new technique to devise high-performance radar waveforms complying with the spectral compatibility requirements.

We develop a polynomial-time procedure to handle a class of generalized fractional programming (GFP) problems with Toeplitz-Hermitian quadratics exploiting the linear matrix inequality (LMI) representation of the finite autocorrelation sequences cone, the spectral factorization theorem, and the Dinkelback's algorithm. For the special case of fractional quadratic programming (FQP) problems, we also provide a SemiDefinite programming (SDP) reformulation of the resulting non-convex optimization by means of the Charnes-Cooper transformation. Finally, we focus on an interesting radar signal processing application to assess the effectiveness of the devised optimization tool.

This letter deals with adaptive radar detection of targets embedded in Gaussian clutter plus range-distributed subspace-structured jamming by exploiting the invariance theory. The class of invariant detectors which ensure the constant false alarm rate property has been characterized by designing the maximal invariant statistic for the studied detection problem. The achievement of this letter is the coincidence of the obtained maximal invariant with that derived assuming range-concentrated jamming.

This paper considers the problem of coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar change detection starting from the availability of image pairs exhibiting possible power mismatches/miscalibrations. The principle of invariance is used to characterize the class of scale-invariant decision rules which are insensitive to power mismatches and ensure the constant false alarm rate property. A maximal invariant statistic is derived together with the induced maximal invariant in the parameter space which significantly compresses the data/parameter domain. A generalized likelihood ratio test is synthesized both for the cases of two- and three-polarimetric channels. Interestingly, for the two-channel case, it is based on the comparison of the condition number of a data-dependent matrix with a suitable threshold. Some additional invariant decision rules are also proposed. The performance of the considered scale-invariant structures is compared to those from two noninvariant counterparts using both simulated and real radar data. The results highlight the robustness of the proposed method and the performance tradeoff involved.

Coherent multipolarization synthetic aperture radar (SAR) change detection exploiting data collected from N multiple polarimetric channels is addressed in this letter. The problem is formulated as a binary hypothesis testing problem, and a special block-diagonal structure for the polarimetric covariance matrix is forced to design a detector based on the generalized likelihood ratio test (GLRT) criterion. It is shown that the structured decision rule ensures the constant false alarm rate property with respect to the unknown disturbance covariance. Results on both simulated and real high-resolution SAR data show the effectiveness of the considered decision rule and its superiority against the traditional unstructured GLRT in some scenarios of practical interest.

This letter is focused on the classic problem of testing samples drawn from independent Bernoulli probability mass functions, when the success probability under the alternative hypothesis is not known. The goal is to provide a systematic taxonomy of the viable detectors (designed according to theoretically-founded criteria) which can be used for the specific instance of the problem. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical success probability (a homogeneous scenario) or (ii) different success probabilities (a non-homogeneous scenario) for the observed samples. As a result of the study, a complete summary (in tabular form) of the relevant statistics for the problem is provided, along with a discussion on the existence of the Uniformly Most Powerful (UMP) test. Finally, when the Likelihood Ratio Test (LRT) is not UMP, existence of the UMP detector after reduction by invariance is investigated.

This paper deals with coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar (SAR) change detection assuming the availability of reference and test images collected fromNmultiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem, and the principle of invariance is used to come up with decision rules sharing the constant false alarm rate property. The maximal invariant statistic and the maximal invariant in the parameter space are obtained. Hence, the optimum invariant test is devised proving that a uniformly most powerful invariant detector does not exist. Based on this, the class of suboptimum invariant receivers, which also includes the generalized likelihood ratio test, is considered. At the analysis stage, the performance of some tests, belonging to the aforementioned class, is assessed and compared with the optimum clairvoyant invariant detector. Finally, detection maps on real high-resolution SAR data are computed showing the effectiveness of the considered invariant decision structures.

In this work we propose an adaptive receiver with enhanced range estimation capabilities, which jointly exploits the over-sampling of the noisy returns and the spillover of target energy to adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the Generalized Likelihood Ratio Test (GLRT) is derived and assessed. The performance analysis highlights that better detection performances and increased range estimation accuracies can be achieved exploiting the oversampling at the price of an additional processing cost.

In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-interference-plus-noise ratio (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, we devise a novel method to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property.

In this letter, we devise an adaptive decision scheme for point-like targets capable of handling the joint presence of homogeneous clutter and structured interference in the primary and secondary data. To this end, we resort to a design procedure based on the method of sieves: the usual generalized likelihood ratio test (GLRT) is modified constraining the unknown parameters to belong to a suitable subset of the original space ensuring unique solutions for the involved optimizations. Remarkably, the proposed receiver possesses the constant false alarm rate (CFAR) property with respect to the unknown covariance matrix of the unstructured interference. At the analysis stage, closed-form expressions for the false alarm and detection probabilities are derived.

This letter considers constrained steering direction estimation in the presence of additive Gaussian disturbance. The uncertainty region is modeled through double-sided quadratic constraints (up to three) and the Maximum Likelihood (ML) criterion is adopted to get the direction estimator. It is shown that the considered formulation leads to a fractional Quadratically Constrained Quadratic Program (QCQP) whose solution can be computed in polynomial time via semidefinite programming relaxation, Charnes-Cooper transformation, and suitable rank-one decomposition tools. At the analysis stage, with reference to a specific constraint set, the performance of the devised estimator is compared with the constrained Cramer Rao lower Bound (CRB).

In this work, we devise an adaptive decision scheme with range estimation capabilities for point-like targets in partially homogeneous environments. To this end, we exploit the spillover of target energy to consecutive range samples and synthesize the Generalized Likelihood Ratio Test. The performance analysis, conducted resorting to both simulated data and real recorded datasets, highlights that the newly proposed architecture can guarantee superior detection performance with respect to its competitors while retaining accurate estimation capabilities of the target position.

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space/deformation-velocity (4-D) analysis. It extends SAR interferometry and differential interferometry concepts, offering new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting scatterers showing partial correlation properties induced by simultaneous acquisitions from satellite formations or an uneven temporal distribution of satellite constellations. To this end, we first provide a general model for the pixel imaged by a multidimensional SAR system. Then, we design a constant false alarm rate (CFAR) decision rule accounting for the presence of partially coherent scatterers. At the analysis stage, we assess the performance of the new detector also in comparison with a previously proposed CFAR scheme, developed in the context of SAR tomography for fully coherent scatterers.

Synthetic aperture radar (SAR) tomography is a technique that extends the concept of SAR interferometry for the accurate localization and monitoring of ground scatterers. Data that are being acquired by the new high resolution SAR sensors offer new perspectives in the 3-D reconstruction and monitoring of urban areas and, particularly, of individual buildings. SAR tomography allows increasing the density of measurements by handling situations where multiple stable scatterers interfere in the same resolution cell. The detection of reliable, i.e., persistent, scatterers is however a challenging issue. In this paper, we investigate three detection approaches: The first is based on a modification of information theoretical criteria; the last two are based on the generalized likelihood ratio test. Theoretical performances are analyzed in details on simulated data, and results of the application to real data from both medium and very high resolution sensors are also provided.

Information-theoretic performance analysis of a MIMO communication over Land Mobile Satellite (LMS) channels, under ergodic and non-ergodic regimes, is performed. The capacity-achieving input covariance matrix, and the corresponding ergodic capacity, assuming perfect receive-side information but making different assumptions on the amount of channel knowledge at the transmitter, are derived. We obtain exact results, but for the case when perfect channel knowledge is assumed at both ends of the link, for which we provide an upper bound to the ergodic capacity. In the non-ergodic scenario, we compute the outage capacity in absence of power-control, and discuss the asymptotic Gaussianity of the mutual information, which strongly depends on the overall number of degrees of freedom available on the channel. Design guidelines for multiantenna LMS channels are gained studying the low Signal-to-Noise Ratio (SNR) behavior of the capacity, still under the assumption of absence of knowledge of the channel matrix (or its statistics) at the transmitter. The results are illustrated through several examples, aimed at assessing the impact on the performance of the diversity order and/or the Line-of-Sight (LOS) fluctuations.

A statistical model for Land Mobile Satellite (LMS) channels, where transmitters and receivers are equipped with multiple antennas, is introduced. Several spectral statistics are given, which allow the theoretical performance analysis of the newly proposed channel model from both a communication and an information-theoretic point of view. Specifically, joint and marginal statistics of the squared singular-values of the channel matrix are evaluated, paving the way for the performance analysis under ergodic and nonergodic assumptions on the channel behavior. The capacity-achieving input covariance matrix, and the corresponding ergodic capacity, assuming perfect receive-side information but making different assumptions on the amount of channel knowledge at the transmitter, are derived. We obtain exact results, but for the case when perfect channel knowledge is assumed at both ends of the link, for which we provide an upper bound to the ergodic capacity. In the nonergodic scenario, we compute the outage probability in absence of power-control, and discuss the asymptotic Gaussianity of the mutual information, which strongly depends on the overall number of degrees of freedom available on the channel. Design guidelines for multiantenna LMS channels are gained studying the low signal-to-noise ratio (SNR) behavior of the capacity, still under the assumption of absence of knowledge of the channel matrix (or its statistics) at the transmitter. The results are illustrated through several examples, aimed at assessing the impact on the performance of the diversity order and/or the line-of-sight (LOS) fluctuations.

Multi-Dimensional (3D/4D) SAR imaging (SAR Tomography and Differential SAR Tomography) allows the localization and monitoring of ground scatterers, even interfering in the same azimuth-range pixel. Indeed, the presence of multiple scatterers has shown to affect even the performances of high resolution radar systems. In this paper we discuss two strategies for the detection of interfering scatterer pairs. The first one is based on the extension of the GLRT test already proposed for the detection of single scatterers, the second one is based on the BIC criteria commonly used in the contest of model order selection. Performances of the two decision schemes are evaluated on simulated data.

The problems of detecting the presence of a new user and of estimating the delays of its multipath replicas in a direct-sequence/code-division-multiple-access (DS/CDMA) system are investigated. Despite previous works, we consider a doubly-dispersive fading channel model and we propose a new code-aided detection algorithm which relies on the application of a powerful statistical tool known as the method of sieves. The proposed detector is blind and bounded constant false alarm rate. As a byproduct of the detection stage, a new blind procedure to estimate the multipath channel delays of the detected user is also derived.

In this paper, we consider the problem of detecting an unknown signal, belonging to a conic region, in the presence of complex Gaussian disturbance. We assume that the cone aperture parameter, which ranges in a pre-assigned interval, is unknown. Hence, we devise two decision rules which are respectively based on the Generalized Likelihood Ratio Test (GLRT) and on the Multifamily Likelihood Ratio Test (MFLRT). Both the receivers ensure the Constant False Alarm Rate (CFAR). At the analysis stage, we compare the performance of the two decision strategies in correspondence of different parameters for the useful signal component.

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space deformation-velocity (4-D) analysis. It is an extension of the concepts of SAR interferometry and differential interferometry SAR and offers new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting single scatterers for localization and monitoring issues. To this end, we resort to a constant false alarm rate (CFAR) detection scheme which can be synthesized according to three different design criteria: generalized likelihood ratio test, Rao test, and Wald test. At the analysis stage, the performance of the aforementioned detector is compared to that of a previously proposed CFAR scheme, based on the multi-interferogram complex coherence and widely used in persistent scatterer interferometry. The analysis is conducted both on simulated and on real SAR data, acquired by ERS-1/2 satellites. Finally, Cramer-Rao lower bounds for the estimation of the scatterer elevation and velocity are provided.

In this work, a MIMO detection problem is considered. At first, we derive the Generalized Likelihood Ratio Test (GLRT) for arbitrary transmitted signals and arbitrary time-correlation of the disturbance. Then, we investigate design criteria for the transmitted waveforms in both power-unlimited and power-limited systems and we study the interplay among the rank of the optimized code matrix, the number of transmit diversity paths and the amount of energy integrated along each path. The results show that increasing the rank of the code matrix allows generating a larger number of diversity paths at the price of reducing the average signal-to-clutter level along each path.

This letter deals with the problem of detecting a signal known up to a scaling factor in the presence of partially homogeneous Gaussian disturbance with unknown covariance matrix. It is proved that the Rao test and the Wald test coincide with the generalized likelihood ratio test (GLRT) previously derived. Otherwise stated, the Rao test, the Wald test, and the GLRT are all equivalent to the uniformly most powerful invariant (UMPI) detector.

This letter deals with the problem of adaptive detection in Gaussian disturbance with unknown but persymmetric structured covariance matrix. A partially-homogeneous environment is considered at the design stage, and a receiver based on the generalized likelihood ratio test (GLRT) is derived. At the analysis stage, the performance of the new receiver is assessed, also in comparison with the unstructured GLRT, showing that thea-prioriinformation on the covariance structure can lead to a noticeable performance improvement.

The distribution of the sum of non-negative random variables plays an essential role in the performance analysis of diversity schemes for wireless communications over fading channels. While for common fading models such as the Rayleigh, Rice, and Nakagami, the performance of diversity systems is well understood, a minor attention has been devoted to the shadowed-Rice (SR) case, namely a Rice fading channel with fluctuating (e.g. random) Line of Sight (LOS) component. Indeed, the analytical performance evaluation of diversity systems on SR fading channels requires the availability of handy expressions for the distribution of the combined received power. To this end, the rationale of this paper is twofold: first, to evaluate the distribution of the sum of SR random variables, both for the case of independent as well as correlated LOS components, and then to carry out an extensive performance analysis of maximal ratio combining (MRC) detection scheme on SR fading channels.

This paper addresses adaptive radar detection of distributed targets embedded in noise plus interference assumed to belong to an either known or unknown subspace of the observables. We assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as zero-mean, complex normal ones, sharing the same structure of the covariance matrix up to possibly different power levels between primary and secondary data. The common structure and the power levels are unknown at the receiver. The performance assessment confirms the effectiveness of the newly-proposed detection algorithms also in comparison to previously-proposed ones

We focus on the synthesis and the analysis of recursive receivers for direct-sequence code-division multiple-access systems aimed at achieving satisfactory performance at the price of a moderate computational complexity. At the analysis stage, some interesting properties shared by the proposed procedures are proven. Finally, the performance assessment shows that the new schemes are superior to the linear detectors, and some of them achieve a bit-error rate close to that of the optimum receiver.

This paper deals with the problem of detecting a signal known up to a scaling factor in the presence of Gaussian disturbance with unknown covariance matrix. We propose a novel derivation of the adaptive matched filter (AMF) previously designed in a previous paper by Robey et al.. Precisely, we show that the Wald test for the problem at hand coincides with the AMF.

Previous results on blind multiuser detection apply in situations where the signal parameters of the users of interest are known, and those of the interferers; are unknown. In this paper, we consider the new paradigm of an N-user system, in which K users are active, and the problem is to detect G users of interest out of those K active users when the signal parameters (codes, amplitudes) of the G users of interest are known, as are the codes of all N users. What is not known at the receiver, however, is K - G, the number of active interferers, and the identity of these interferers. A solution to such a problem could be to ignore the knowledge of the remaining N - G codes, and apply known blind multiuser detectors based on stochastic approximation or subspace tracking techniques. However, it is shown here that the additional knowledge of those codes can be used to obtain an interference-identification-based blind multiuser receiver that has much faster convergence properties. We illustrate the underlying principle in the context of blind group detection in synchronous direct-sequence/code-division multiple-access (DS/CDMA) systems operating in channels that exhibit frequency-selective fading.

Adaptive detection of multidimensional signals in the presence of interference with unknown covariance matrix is an expanding topic in a variety of scenarios ranging from radar/sonar to digital communication systems. We attack the problem of detecting a multidimensional radar signal, modeled as an unknown N/spl times/H matrix, embedded in Gaussian noise with unknown covariance matrix, with the ambition of devising receivers which yield the constant false alarm rate (CFAR) property. We show that this aim can be achieved by resorting to the principle of invariance, namely restricting our attention to hypothesis testing problems which remain unaltered under a proper group of transformations. Several detectors based on the maximal invariant statistic are studied and, in particular, the generalized likelihood ratio test (GLRT) is shown to belong to the class of invariant tests.

In this work we consider the problem of M-ary signal detection over a single-input-multiple-output channel affected by time- and/or frequency-dispersive Rayleigh-distributed fading and in the presence of non-Gaussian noise, modeled as a spherically invariant random process (SIRP). We derive the optimum (minimum error-probability) detector, and show that its structure is canonical, i.e. it is independent of the actual noise statistics. Finally, the performance analysis of the receiver highlights that the adoption of diversity represents a suitable means to restore performance also in the presence of dispersive fading and impulsive non-Gaussian noise.

An adaptive detector of range-spread targets embedded in Gaussian noise with unknown covariance matrix is developed and assessed. The proposed receiver requires no prior knowledge about the target strength and its scattering geometry and achieves constant false alarm rate with respect to the noise covariance matrix.

We present a fully adaptive detector of coherent pulse trains embedded in compound-Gaussian clutter, whose covariance matrix is a circulant one. Remarkably, the proposed receiver ensures CFAR-ness with respect to the non-Gaussian noise distribution as well as to its temporal correlation. It also exhibits an acceptable loss with respect to previously proposed nonadaptive structures, even for small sizes of the estimation sample.