Search

A new algorithm for passive bistatic radar (PBR) localization is proposed via joint exploitation of multiple illuminators of opportunity and measurements gathered by a colocated active radar. Ad-hoc constraints within the localization process are bestowed accounting for both a priori information on the PBR receive antenna main-beam size and the uncertainty characterizing active radar data, i.e., the root mean square error (RMSE) of range/bearing measurements. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained least squares framework. The resulting non-convex optimization problem is globally solved providing a closed-form estimate in Cartesian coordinates. The performance of the proposed estimator, in terms of RMSE, exhibits sensible improvement with respect to counterparts. The proposed technique is also applied to the case of a dynamic scenario for a bi-sensor surveillance system, where the active rotating platform acquires measurements at each scanning period. The results show the localization performance improvement achieved when the system is complemented with a PBR staring in a specific search sector of interest.

This paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Both noise and clutter interference cases (pulse length-limited and beamwidth-limited surface clutter as well as volume clutter) are considered. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with the data acquisition procedure and the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations., Comment: typo corrected in the first equation on page 6

This paper is focused on the detection and resolution of Unmanned Aerial Vehicles (UAVs) via a multi-channel phased-array radar with emphasis on the challenging situation where multiple targets are unresolved in the range-Doppler domain and closely spaced in angle. An adaptive processor is devised which, after triggering a detection in a given range-Doppler cell, implements a binary decision about the presence of a single or multiple targets. In the former situation, the plain monopulse algorithm is used to estimate the target azimuth whereas, in the latter case, a super-resolution technique is activated to get the angular parameters of the UAVs. The performance of the signal processor is assessed both on simulated and on measured data (endowed by ground-truth) collected by a 12-channel phased-array radar operating in the C-band. The results highlight the capabilities of the architecture to detect the presence of targets, to discriminate the single from the multiple targets scenario, and to resolve the UAVs in angle domain when they are unresolved in range and Doppler.

This article considers target separation detection (TSD) in the presence of homogeneous Gaussian interference. The problem is formulated as a hypothesis test for two typical situations: 1) the use of a low range-resolution radar or fast-track update rate; 2) the use of a high-resolution radar or low-track update rate. At the design stage, TSD tests are devised according to the generalized likelihood ratio test criterion. The computation of each decision statistic requires the solution of a semidefinite programming problem obtained leveraging the relationship among linear matrix inequalities and nonnegative trigonometric polynomials. All the obtained decision rules ensure the bounded constant false alarm rate property. At the analysis stage, the performance of some benchmark detectors is given in terms of detection and false alarm probabilities. Finally, numerical examples are provided to show the performance of the proposed tests as compared with the benchmarks as well as the gain achievable over some counterparts devised to monitor a separation event.

Radar operation in a spectrally dense environment is nowadays a very challenging problem due to the increasing demand of spectral resources for defence/surveillance applications, remote sensing capabilities, and civilian wireless services. This article explores the technical feasibility of radar waveforms capable of ensuring spectral coexistence with overlaid emitters via a modern digital arbitrary waveform generator. To this end, a specific hardware-in-the-loop test bed is designed to mimic the perception-action cycle necessary for the agile management of the radio spectrum. Then, a spectrum analyzer (SA) and a digital oscilloscope (DO) are used to demonstrate the compliance of the synthesized waveforms with the theoretical counterparts. In particular, the SA is used to establish if the synthesized waveforms fulfil the spectral requirements forced at the design stage. The DO, instead, is used to assess the adherence of synthesized signals ambiguity function (AF) with the theoretical one. For the considered case studies, results highlight that the synthesized waveforms enable spectral compatibility between radar and communication systems and exhibit desirable AF properties.

Maximum likelihood (ML) estimation of the loading factor under affine constraints on the covariance eigenvalues is addressed. Several situations of practical interest for radar are considered, and the corresponding ML solutions to the loading factor estimation problem are derived in closed form. Furthermore, it is shown that the constrained ML problem, the constrained geometric approach, and the constrained problem of mean square error minimization (with respect to the loading factor) all lead to the same solution. At the analysis stage, the effectiveness of the resulting covariance estimators is evaluated in terms of both the signal-to-interference-plus-noise ratio and the receiving beampattern shape and compared with that of other covariance estimation methods available in the literature. Finally, a receiving architecture based on the adaptive matched filter that exploits the new loaded covariance estimators is also considered to assess the benefits of the new strategies in terms of detection probability.

Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function is developed resorting to the expectation-maximization algorithm. The corresponding convergence properties are thoroughly established and the rate of convergence is analyzed. The estimation technique is contextualized for two practically relevant radar problems: beamforming and detection of the number of sources. In the former case an adaptive beamformer leveraging the EM-based estimator is presented; in the latter, detection techniques generalizing the classic Akaike information criterion, minimum description length, and Hannan–Quinn information criterion, are introduced. Numerical results are finally presented to corroborate the theoretical study.

This article deals with the problem of adaptive target detection in the presence of homogeneous Gaussian interference with frequency diverse array multiple-input multiple-output radar. Adaptive detectors are devised according to the generalized likelihood ratio test criterion, where the position of the target within each range cell is assumed unknown. To obtain the maximum likelihood estimate of the target incremental range under the $H_1$ hypothesis, three different optimization strategies are pursued. They are, respectively, based on semidefinite programming, discrete grid search, and Newton method. At the analysis stage, a detection performance comparison is carried on among the new proposed adaptive detectors, benchmark, and mismatched receivers. Numerical results corroborate the effectiveness of the developed receivers.

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.

This paper deals with the effects of Power Amplifiers (PAs) on radar waveforms designed to ensure spectral compatibility with other Radio Frequency (RF) wireless systems cohabitating in the same frequency band. To this end, a specific hardware-in-the-loop test-bed composed of a Personal Computer (PC), an Arbitrary Waveform Generator (AWG), a nonlinear PA, and a Signal Analyser (SA) is designed. It allows to gather measurements of the amplified signal and to grasp unwanted modifications of its spectral features due to the PA nonlinear distortion. Besides, it permits a quantitative assessment of the discrepancies between the nominal and the actual interference power injected by the radar in the shared frequency bandwidths. To alleviate the spectral distortions induced by the PA and improve spectral coexistence, the use of a Digital Pre-Distortion (DPD) stage upstream the PA is investigated. Modeling techniques together with parameters inference are discussed and applied to the measured waveforms. The obtained experimental results highlight that pairing a suitable waveform design strategy with a DPD processing represents a viable means to mitigate the PA distortions and to realize spectral coexistence.

A new algorithm for 3D localization in multiplat-form radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Square (LS) optimization problem which is globally solved in a quasi-closed-form leveraging Karush-Kuhn-Tucker (KKT) conditions. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower Root Mean Square Error (RMSE) than counterpart methodologies, especially in the low Signal to Noise Ratio (SNR) regime.

Unmanned Aerial Vehicles (UAVs) have become widely used in civilian and military applications. There is a recognised need for UAVs surveillance against their malicious use. In this context, radar has emerged as a powerful tool in detecting and tracking UAVs. However, the task is quite complicate due to the low-altitude, low velocity and low radar cross section of small UAVs. In this paper, a smart radar signal processing solution in the presence of multiple unresolved UAVs is devised and applied to UAVs surveillance, which fully capitalizes on the coherent integration, constant false alarm rate detection, target multiplicity test and sparse methods to achieve low computational complexity and high angular resolution. At the analysis stage, numerical simulations are carried out to highlight the capability of proposed solution to detect UAVs and resolve them when they are closely spaced.

This paper deals with the synthesis of constant modulus waveforms that optimize radar performance while satisfying multiple spectral compatibility constraints. For each shared band, a precise control is imposed on the injected interference energy. Furthermore, the compliance with amplifiers operating in saturation is ensured at the design stage where phase-only waveforms are considered. To tackle the resulting NP-hard optimization problem, an iterative procedure based on the coordinate descent method is introduced. The overall computational burden of the algorithm is linear with respect to the code length as well as the number of iterations and less then cubic with reference to the number of spectral constraints. Hence, some case studies are reported to highlight the effectiveness of the technique.

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 paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with a discussion on the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations.

The problem of missing sensor measurements can emerge in a variety of radar signal processing applications as for instance beamforming, direction of arrival estimation, interference cancellation, and target detection. The mentioned applications rely on reliable data covariance matrix estimates and suitable procedures, based on the expectation-maximization (EM) algorithm, have been proposed in the open literature to cope with the lack of some entries within specific spatial snapshots. In this paper, the effectiveness of a recent structured covariance matrix estimator [1], accounting for missing data and leveraging possible structural knowledge, is assessed on measured data. Specifically, the estimation procedure is framed in the context of two practically relevant radar applications: beamforming and detection of the number of sources. At the analysis stage, results highlight the effectiveness of the procedure to tackle missing data in the considered radar scenarios.

This paper is focused on the design of phase sequences with good (aperiodic) autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL). The problem is formulated as a bi-objective Pareto optimization forcing either a continuous or a discrete phase constraint at the design stage. An iterative procedure based on the coordinate descent method is introduced to deal with the resulting optimization problems which are non-convex and NP-hard in general. Each iteration of the devised method requires the solution of a non-convex min-max problem. It is handled either through a novel bisection or an FFT-based method for the continuous and the discrete phase constraint, respectively. Additionally, a heuristic approach to initialize the procedures employing the lp-norm minimization technique is proposed. Simulation results illustrate that the proposed methodologies can outperform some counterparts providing sequences with good autocorrelation features especially in the discrete phase/binary case.

The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of model order selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria, are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.

The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to minimize the number of shoots per kill. For this reason, a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper, a model and a robust framework is developed, which incorporates different micro-Doppler-based classification techniques. The reliability of the proposed framework is tested on both simulated and real data.

This letter 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.

Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic learning strategy to track both the covariance matrix of data and its structure (class). We assume that, given the class, the posterior distribution of the covariance is described through a mixture of inverse Wishart distributions, while the class evolves according to a Markov chain. Hence, we devise a novel and general filtering strategy, called multi-class inverse Wishart mixture filter, able to capitalize on previous observations so as to accurately track and estimate the covariance. Some case studies are provided to highlight the effectiveness of the proposed technique, which is shown to outperform alternative methods in terms of both covariance estimation accuracy and probability of correct model selection. Specifically, the proposed filter is compared with class-clairvoyant covariance estimators, e.g., the maximum likelihood and the knowledge-based recursive least square filter, and with the model order selection method based on the Bayesian information criterion.

This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion (both one-step and two-step) where decision statistics leveraging the Maximum Likelihood Estimates (MLEs) of the parameters are compared with a detection threshold. If crossed, target presence is declared and the MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view, ML estimation involves a constrained fractional quadratic optimization problem whose optimal solution can be found via the Dinkelbach's algorithm or approximated through a fast-converging procedure based on a Coordinate Descent (CD) optimization. The performance analysis of the proposed architectures as well as the corresponding discussion is developed in terms of computational complexity, Constant False Alarm Rate (CFAR) behavior, detection performance, and angular estimation accuracy, also in comparison with some counterparts available in the open literature and theoretical benchmark limits.

A new algorithm for Passive Bistatic Radar localization is proposed capitalizing measurements gathered by a colocated active radar and exploiting multiple illuminators of opportunity. Prior information, related to the Passive Bistatic Radar (PBR) receive antenna main-beam size and to the uncertainty characterizing active radar data, is accounted for formalizing ad-hoc constraints for the localization process. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained Least Squares (LS) framework. The performance of the proposed estimator exhibits sensible improvement with respect to some counterparts.

A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and Model Order Selection (MOS) rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated data to highlight the benefits of the approach.

This paper deals with the joint design of transmit waveform and receive filter improving radar Signal to Interference plus Noise Ratio (SINR) in signal-dependent interference and spectrally contested-congested environments. The injected interference energy on each common band is precisely controlled for spectral compatibility. Besides, the transmit signal complies with constant envelop and similarity constraints to bestow some attractive waveform characteristics. To handle the resulting transceiver design problem, an iterative optimization procedure with a polynomial computational complexity is developed leveraging the Coordinate Descent (CD) framework. Finally, numerical results are provided to highlight the effectiveness of the proposed joint transmitter and receiver design technique.

The paper is aimed at discussing the role of cognition in radar sensing providing a system level point of view. In this respect some fundamental issues are considered involving transmitter, receiver, signal and data processing units. Firstly, the definition of the cognitive radar concept is provided and its key aspects are pinpointed. Secondly, the role played by the perception-action cycle and the awareness gleaned by dynamic databases and/or sensing networks is emphasized. Hence the application of the cognitive paradigm for radar operation in spectrally crowded environments is discussed and an example with the use of real hardware is shown. Finally a glimpse on the role of cognition in agile tracking systems is given.

In the present chapter, we have addressed radar waveform optimization in spectrally crowded scenarios via constrained quadratic programming techniques. First of all, we have defined a taxonomy of performance measures which are to be optimized and/or controlled in the waveform synthesis: SINR, signal energy, similarity constraint, interference constraint for spectral compatibility, and quality constraint for bandwidth selection. Then, we have formulated the radar waveform design as a nonconvex QCQP problem and have exploited a specific methodology leveraging rank-one matrix decomposition tools to prove the hidden convexity of some instances of the waveform optimization or to build solution procedures ensuring good quality radar waveforms.

Adaptive target detection in the presence of Gaussian interference considering a frequency diverse array multipleinput multiple-output (FDA-MIMO) radar is investigated. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio test (GLRT) criterion, where the target range is assumed unknown within the radar cell. Hence, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

This paper deals with the problem of adaptive detectors design for a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio (GLR) criterion, where the target range is assumed unknown within the radar cell. In this respect, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid of the possible values or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

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.

This paper deals with the robust joint design of a 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.

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.

This paper considers the design of robust filters for radar pulse-Doppler processing when the interference is a wide sense stationary random process. The figure of merit which is optimized is the signal-to-interference-plus-noise ratio (SINR) at the filter output under a multitude of constraints accounting for Doppler filter sidelobes as well as uncertainties both in the received useful signal component and interference covariance matrix. The design is analytically formulated as a constrained optimization problem whose solvability is thoroughly studied. Precisely, a polynomial-time solution technique to get the optimal filter is proposed exploiting the representation of non-negative trigonometric polynomials via linear matrix inequalities, the spectral factorization theorem, and the duality theory. Last but not least, a detailed analysis of the optimum filter performance is provided showing the tradeoffs involved in the design and the gain achievable over some already known counterparts.

Random and unwanted fluctuations, which perturb the phase of an ideal reference sinusoidal signal, may cause significant performance degradation in radar systems exploiting coherent integration techniques. To quantify the resulting performance loss, we develop a fast-time/slow-time data matrix radar signal representation, modeling the undesired phase fluctuations via multivariate circular distributions and describing the phase noise power spectral density (PSD) through a composite power-law model. Hence, we accurately predict the performance degradation experienced by moving target indication (MTI) algorithms for clutter cancellation, providing a closed form expression for the improvement factor I. The subsequent analysis shows that phase noise affects I directly through its characteristic function (CF). Additionally, I shares a robust behavior with respect to the actual phase noise multivariate circular distribution, as long as the phase noise PSD correctly represents the available measurements.

Random and unwanted fluctuations that perturb the phase of an ideal reference sinusoidal signal may cause significant performance degradation in radar systems employing coherent integration techniques. In this second part of the study, resorting to the fast-time/slow-time data matrix representation developed in "Part I" of this two-part study, we assess the performance of both pulse Doppler processing (PDP) algorithms and sidelobe blanker (SLB) techniques when phase noise and Gaussian interference (clutter plus noise) impair the data. Specifically, we derive analytically manageable expressions for: 1) the probability of false alarm and the probability of detection of PDP algorithms; 2) the probability of false alarm, the probability of blanking a coherent repeater interference, and the probability of blanking a target in the mainlobe of SLB processors. Simulation results show that phase noise may slightly degrade the performance of PDP and SLB processors as long as its power spectral density correctly represents the available measurements. Additionally, the matched filter receiver ensures a high level of robustness against phase noise, highlighting its robustness against steering and covariance matrix mismatches, a property that we formally prove in the paper.

In this chapter, cognition about the surrounding environment has been exploited to adapt the system to the interfering environment. Precisely, the robust joint optimization of the transmit signal and receive filter bank in the presence of signal-dependent interference has been considered. The Doppler shift of the moving target has been assumed unknown, and the worst case SINR at the output of the filter bank has been employed as figure of merit. By doing so, the effectiveness of the cognitive architecture has been investigated when the target Doppler frequency is a-priori unknown. In fact, while a rough Doppler knowledge is very reasonable during the detection confirmation or for an already tracked target, it is usually not available during the standard search radar operation and suitable cognitive algorithms are required.

This paper deals with the synthesis of waveforms that optimize radar performance while satisfying multiple spectral compatibility constraints. For each shared band, precise control is imposed on the injected interference energy. Furthermore, compliance with amplifiers operating in saturation is ensured at the design stage where phase-only waveforms are considered. To tackle the resulting NP-hard optimization problem, an iterative procedure based on the coordinate descent method is introduced. Hence, some case studies are reported to highlight the effectiveness of the technique.

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations.

This paper proposes a new radar detection architecture, which is composed of an interference covariance structure classifier and a bank of adaptive radar detectors. The classifier is based on model order selection theory and the Bayesian information criterion to determine the covariance structure that is deemed to be suitable for a specific set of radar data [1] . This decision stage drives the choice of the radar detector within a specific class of adaptive detectors to establish the possible target presence. The critical issue concerning the constant false alarm rate behavior of the architecture is discussed and two techniques for the threshold setting process are proposed. Finally, the detection performance analysis, conducted on both simulated and measured data, shows that the proposed architecture can guarantee better performance than classic radar decision schemes in scenarios where the interference covariance exhibits structural properties.

Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic environment learning strategy to track both the covariance matrix and its class; the class represents a set of structured covariance matrices. We assume that the posterior distribution of the covariance given the class, is basically a mixture of inverse Wishart, while the class posterior distribution evolves according to a Markov chain. The proposed multi-class inverse Wishart mixture filter is shown to outperform the class-clairvoyant maximum likelihood estimator in terms of covariance estimate accuracy, as well as the Bayesian information criterion rule in terms of classification performance.

Model Order Selection (MOS) rules are exploited to devise two adaptive architectures for multiple noise-like jammer detection which process clutter-free data. Specifically, the former contains a MOS-based stage, that makes inference on the number of jammers, and a detection stage, that confirms the presence of the jammers suitably exploiting the estimates provided by the first stage. On the other hand, the latter architecture, which relies on the Modified Likelihood Ratio Test proposed in [1], jointly performs detection and estimation. Remarkably, both guarantee the constant false alarm rate property with respect to the thermal noise power. At the analysis stage, the performance of the proposed architectures are investigated highlighting the interplay among the different parameters.

The two-dimensional (2-D) spectrum sensing problem is addressed in this paper. Specifically, assuming a sensor equipped with multiple receive antennas, three signal processing techniques able to recover the space-frequency occupancy map are proposed. At the analysis stage, an interesting case study is reported to compare the different strategies and highlight the effectiveness of the developed procedures.

We deal with robust adaptive beamforming (RAB) for factored radar space-time adaptive processing (STAP) as a worst-case signal-to-interference-plus-noise ratio (SINR) maximization, because of mismatches in the desired signal and the interference-plus-noise covariance. To this end, we first reformulate the (beamformer unstructured) RAB problem as a second-order cone program (SOCP), and forcing the factored structure in the STAP weight vector, we come up with a new problem solved via an alternating optimization method. Second, we transform the worst-case SINR maximization into a biquadratic matrix inequality (BQMI) problem. We show that there is a closed-form solution for the BQMI problem, which implies that the robust optimal choice for the factored STAP processor is the Kronecker product of the spatial and temporal steering vectors. This is the most important finding of this paper suggesting that in the presence of steering and covariance mismatches (following the considered model), it is convenient to avoid adaptivity and to just use the non-adaptive beamformer.

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.

Purpose of the Tutorial: to give a rigorous and academic point of view on the fundamentals of radar detection, receiver performance analysis, Monte Carlo simulation of radar receivers, and CFAR algorithms. The idea is to provide a formative and methodological tutorial on the above topics which represent the base to address more challenging radar detection problems.

This chapter has been devoted to the design of a tracker exploiting cognition at multiple levels. Specifically, environmental maps and characteristics of the targets, available in the dynamic database possibly learned from the feedback channel, have been used to gain improved tracking performance in a multiple targets scenario exploiting measurements provided by a tracking radar. Unlike the conventional tracking radar (which is very sensitive to false alarms and/or missed detections), the main advantage of the cognitive paradigm is the significant reduction in the number of false alarms, missed detections, false tracks, and improved true target track life. In the second part of the chapter, the focus has been on waveform selection to optimize the target tracking process. Specifically, it has been assumed that a waveform library is available at the transmitter, and the most suitable signal (in the sense of minimizing the predicted tracking estimation error) is chosen for the next dwell. The proposed algorithm is based on the use of feedback information from the receiver and exploits a standard KF. The performance of the proposed strategy has been studied in a challenging scenario accounting for a maneuvering target in the presence of thermal noise only or RF interference plus thermal noise. The results have highlighted that the adaptive feedback process guiding the waveform selection is able to provide advantages over the classic radar tracker, which does not resort to transmit adaptivity.

A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, an efficient solution technique to handle the resulting constrained optimization problem is developed. Specifically, it is shown that the new family of distribution-free estimators shares a shrinkagetype form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. For the two most common norm instances, i.e., Frobenius and spectral, very efficient algorithms are developed to solve the aforementioned one-dimensional optimization leading to almost closed form covariance estimates. At the analysis stage, the performance of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) both for a spatial and a Doppler processing assuming different data statistical characterizations. The results show that interesting SINR improvements with respect to some counterparts available in the open literature can be achieved especially in training starved regimes., Comment: submitted for journal publication