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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.

Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF). © 2014 IEEE.

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

In this paper a performance analysis on recorded live data of some detectors for range-spread targets is developed. To this end, real target and sea-clutter data collected by a fully coherent Ka-band radar system, featuring submeter range resolution, are used. The study is of particular interest for homeland security radar applications where a careful coastal control is necessary to prevent the arrival of nonauthorized small boats. The performance of both rank-one and subspace range-spread target detection strategies is analyzed, both in terms of constant false alarm rate (CFAR) behavior and in terms of detection capabilities. With reference to the former issue, clutter-only datafiles are used whereas, concerning the latter data containing both real target and clutter are used. The targets returns come from typical small boats (such as inflatable, wooden, and patrol boats) appearing range distributed at the resolution of the exploited radar system. Range-time detection maps are shown, assessing the capability of the analyzed processors to detect the aforementioned targets of great interest for homeland coastal security. Finally, the performance improvements achievable by over-resolving the target is quantified.

This correspondence considers the problem of robust waveform design in the presence of colored Gaussian disturbance under a similarity and an energy constraint. We resort to a max-min approach, where the worst case detection performance (over the possible Doppler shifts) is optimized with respect to the radar waveform under the previously mentioned constraints. The resulting optimization problem is a non-convex Quadratically Constrained Quadratic Program (QCQP) with an infinite number of constraints, which is NP-hard in general and typically difficult to solve. Hence, we propose an algorithm with a polynomial computational complexity to generate a good sub-optimal solution for the aforementioned QCQP. The analysis, conducted in comparison with some known radar waveforms, shows that the sub-optimal solutions by the algorithm lead to high-quality radar signals.

This paper considers the problem of adaptive radar detection in Gaussian clutter with unknown spectral properties. We employ a Bayesian approach based on a suitable model for the probability density function (pdf) of the unknown clutter covariance matrix. We devise two detectors based on the generalized likelihood ratio test (GLRT) criterion both one-step and two-step. The suggested decision rules achieve the same performance as the non-Bayesian GLRT detectors when the size of the training set is sufficiently large. However, our new detectors significantly outperform their non-Bayesian counterparts when the training set is small. The analysis is also supported by results on real L-band clutter data from the MIT Lincoln Laboratory phase one radar and on high fidelity radar data from the knowledge-aided sensor signal processing and expert reasoning (KASSPER) program.

This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of phase coded pulse trains and determine the radar code which approximately maximizes the detection performance under a similarity constraint with a prefixed radar code. This is tantamount to forcing a similarity between the ambiguity functions of the devised waveform and of the pulse train encoded with the prefixed sequence. We consider the cases of both continuous and finite phase alphabet, and formulate the code design in terms of a nonconvex, NP-hard quadratic optimization problem. In order to approximate the optimal solutions, we propose techniques (with polynomial computational complexity) based on the method of semidefinite program (SDP) relaxation and randomization. Moreover, we also derive approximation bounds yielding a ldquomeasure of goodnessrdquo of the devised algorithms. At the analysis stage, we assess the performance of the new encoding techniques both in terms of detection performance and ambiguity function, under different choices for the similarity parameter. We also show that the new algorithms achieve an accurate approximation of the optimal solution with a modest number of randomizations.

This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of linearly coded pulse trains and determine the optimum radar code according to the following criterion: maximization of the detection performance under a control on the region of achievable Doppler estimation accuracies, and imposing a similarity constraint with a prefixed radar code. This last constraint is tantamount to requiring a similarity between the ambiguity functions of the devised waveform and of the pulse train encoded with the prefixed sequence. The resulting optimization problem is nonconvex and quadratic. In order to solve it, we propose a technique (with polynomial computational complexity) based on the relaxation of the original problem into a semidefinite program. Thus, the best code is determined through a rank-one decomposition of an optimal solution of the relaxed problem. At the analysis stage, we assess the performance of the new encoding technique in terms of detection performance, region of achievable Doppler estimation accuracies, and ambiguity function.

Radar signal design in spectrally dense environments is a very challenging and topical problem. This paper 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.

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.

This paper considers the design of robust filters for radar pulse-Doppler processing when the interference is a wide sense stationary random process. The Signal-to-Interference-plus-Noise Ratio (SINR) at the filter output is considered as the figure of merit to optimize under a multitude of requirements 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. Specifically, 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 trade-offs involved in the design and the gain achievable over some already known counterparts.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the signal-to-interference-plus-noise ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach. Precisely, we model the uncertainty set as a finite collection of TIRs, obtained sampling the actual TIR at some aspect angles. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications, where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method.

In this paper we discuss the combined use of a priori information and adaptive signal processing techniques for the design and the analysis of a knowledge-aided (KA) radar receiver for Doppler processing. To this end, resorting to the generalized likelihood function (GLF) criterion (both one-step and two-step), we design and assess data-adaptive procedures for the selection of training data. Then we introduce a KA radar detector composed of three elements: a geographic-map-based data selector, which exploits some a priori information concerning the topography of the observed scene, a data-adaptive training selector which removes dynamic outliers from the training data, and an adaptive radar detector which performs the final decision about the target presence. The performance of the KA algorithm is analyzed both on simulated as well as on real radar data collected by the McMaster University IPIX radar. The results show that the new KA system achieves a satisfactory performance level and can outperform some previously proposed adaptive detection schemes.

We conduct an experimental analysis for assessing the constant false alarm rate (CFAR) behavior of four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land, lake, and mixed land and sea clutter, collected by two radar systems (the MIT Lincoln Laboratory Phase-One radar and the McMaster IPIX radar) at different polarizations, range resolutions, and frequency bands. The results show that all the receivers, in the presence of real data, don't respect their nominal probability of false alarm (P/sub fa/), namely they exhibit a false alarm rate higher than the value preassigned at the design stage. Nevertheless one of them, the recursive persymmetric adaptive normalized matched filter (RP-ANMF) is very robust, in the sense that it presents an acceptable displacement from the nominal P/sub fa/, in correspondence of all the analyzed scenarios.

In this paper we address the problem of devising and analyzing radar processors for space-time adaptive processing applications. We first define a class of adaptive filters based on the approximation of the overall disturbance with a multichannel autoregressive process. Thus we focus on a specific element of the family which exploits the modified square-root normalized maximum entropy algorithm for estimating the process coefficients. The performance assessment highlights the fast convergent capabilities of the novel processor, which for a limited number of training data, provides performances very close to the optimum filter. Finally we also devise and assess (both on simulated as well as on real radar data) two adaptive detectors which represent interesting solutions to deal with situations where a small number of training data is available. (C) 2004 Elsevier B.V. All rights reserved.

A statistical analysis is presented of real radar clutter data collected using the McMaster IPIX radar in 1998 and stored in the Grimsby database. We first show the deviations of the amplitude statistics from the Rayleigh model and the suitability of the K- and Weibull-distribution for the first-order amplitude statistical characterization. Thus we focus on the I and Q components of the available data and study their statistical compatibility with the compound Gaussian model. Towards. this goal it has been necessary devising appropriate testing procedures; in particular, with reference to the higher order. statistics agreement, we have designed a validation procedure involving the clutter representation into generalized spherical coordinates. Remarkably the results have confirmed the suitability of the spherically invariant random processes (SIRPs) for the correct modeling of the radar clutter. Finally we have performed a spectral analysis highlighting the close matching between the estimated clutter spectral density and the exponential model.

Phase modulation induced by target micro-motions introduces side-bands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that is capable of uniquely describing the micro-motion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists in the extraction of the pseudo-Zernike moments from the Cadence Velocity Diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the Signal-to-Interference plus Noise Ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach based on two different uncertainty sets. The former is composed of a finite collection of TIRs, obtained by sampling the actual TIR at some aspect angles; the latter is a spherical uncertainty set. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method in different scenarios.

We deal with the problem of detecting point-like targets in diffuse multipath environments, modeling the target echo as the superposition of a deterministic signal with an unknown scaling factor (due to the direct path) plus a zero-mean complex circular symmetric Gaussian random vector with an unknown covariance matrix (accounting for the echoes from the glistening surface). We devise a constrained Generalized Likelihood Ratio Test (GLRT) for the resulting hypothesis testing problem, enforcing the primary data covariance matrix (due to both interference and multipath echoes) to belong to a neighborhood of the secondary data sample covariance matrix. Remarkably, the proposed decision scheme ensures the desirable Constant False Alarm Rate (CFAR) property with respect to the unknown parameters of the interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with existing solutions, highlights the effectiveness of the new approach to cope with diffuse multipath phenomena.

In this paper, we address the problem of polarimetric adaptive detection of range-spread targets in Gaussian noise with unknown covariance matrix. At the design stage, we model the target echo from each polarimetric channel as a deterministic signal known up to a scaling factor (possibly varying from cell to cell), which accounts for the polarimetric scattering properties of the target. We first show the failure of the generalized likelihood ratio test (GLRT) procedure to deal with this kind of problem, and thus, we propose a fully adaptive detector based on the method of sieves. We also derive the analytical expression for the probability of false alarm and show that the newly introduced receiver can be made bounded constant false alarm rate (CFAR). Finally, we present simulation results highlighting the performance gain that can be achieved by resorting to polarization diversity in conjunction with high resolution.

Reliable micro-Doppler signature classification requires the use of robust features describing uniquely the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper the application of the pseudo-Zernike moments for micro-Doppler classification is introduced demonstrating the effectiveness of the proposed approach by classifying real data. The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. © 2014 IEEE.

In this paper, we deal with cognitive design of the transmit signal and receive filter optimizing the radar detection performance without affecting spectral compatibility with some licensed overlaid electromagnetic radiators. We assume that the radar is embedded in a highly reverberating environment and exploit cognition provided by Radio Environmental Map (REM), to induce spectral constraints on the radar waveform, by a dynamic environmental database, to predict the actual scattering scenario, and by an Electronic Support Measurement (ESM) system, to acquire information about hostile active jammers. At the design stage, we develop an optimization procedure which sequentially improves the Signal to Interference plus Noise Ratio (SINR). Moreover, we enforce a spectral energy constraint and a similarity constraint between the transmitted signal and a known radar waveform. At the analysis stage, we assess the effectiveness of the proposed technique to optimizing SINR while providing spectral coexistence.

We deal with performance prediction of the incoherent radar receiver (i.e. squared-law plus integrator) in the presence of independent non-identically distributed (non-id) Weibull target echoes. First of all, we provide the probability density function (pdf) for the sum of independent but non-id Weibull random variables in terms of an infinite sum of Gamma pdfs. Then, we develop an analytic expression for the detection probability as a fast converging series of functions. Finally, we evaluate the impacts on the detection performance of non-id Weibull target parameters. © 2014 IEEE.

In this study, the authors derive an adaptive decision scheme with enhanced range estimation capabilities for point-like targets in partially homogeneous environments. To this end, the authors jointly exploit the oversampling of the received signal and the spillover of target energy to consecutive range samples. A suitable discrete-time model for the received signal is introduced and the so-called two-step generalised likelihood ratio test is derived and assessed. The performance analysis, conducted resorting to both simulated data and real recorded datasets, is aimed at assessing the effectiveness of the proposed solution, also in comparison with the existing detectors with range estimation capabilities. The illustrative examples highlight that better detection performances and increased range estimation accuracy can be achieved exploiting oversampling at the price of an additional processing cost.

In the present paper, the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed electromagnetic radiators is addressed. Cognition provided by a Radio Environmental Map (REM) is exploited so as to induces dynamic spectral constraints on the radar waveform. A waveform optimization aiming at improving some radar performances is proposed, whose feasibility is extensively studied. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable Signal to Interference plus Noise Ratio (SINR), spectral shape, and the resulting AutoCorrelation Function (ACF).

A new method to address radar target detection problems in multipath environments is considered. The novelty of the proposed method is that it exploits prior knowledge on the environment and combines it with ray-tracing electromagnetic modelling to determine some information about the possible multipath structure. This information is used to separate the environment into different regions based upon the behaviour of the multipath components. Specifically, as a case study, the authors consider a radar and a target in the presence of a perfectly reflecting planar surface. For this environment, three regions are determined based on the amount of overlap among the multipath components. For each region, receivers are designed exploiting the multipath structure. Thus, a different viewpoint to analyse radar detection problems is suggested. The two main results are the improvement in the target probability of detection, by properly accounting for the multipath and a priori determination of the best performing detector based upon the location of the target and the available signal-to-noise ratio.

We deal with the performance prediction of the incoherent radar receiver in the presence of arbitrary correlated, possibly nonidentically-distributed target backscattered echoes. The problem is of interest in some common scenarios that account for frequency agility, and polarization, as well as for spatial diversity. We develop analytic expressions for the detection probability in terms of function series, assuming that a generalized Swerling-chi distribution models the first order probability density function (pdf) of the target amplitude. Moreover, we study the speed of convergence of the resulting series and assess the impact on the detection performance of both the correlation and the nonidentical distribution of the target returns.

The performance of two detection strategies based on two distinct multipath structures of a radar-target environment is presented under the assumption of a zero-mean complex circular Gaussian noise (CWGN) with known power spectral density (PSD) σ2. Neyman Pearson (NP) and Generalized Likelihood Ratio Tests (GLRT) are considered as optimum and sub-optimum receivers, respectively. One strategy, associated with NP1 and GLRT1 receivers, is considered for highly clumped multipath returns in the time-domain, while the other strategy, associated with NP2 and GLRT2 receivers, which are exploiting multipath in the target detection, is taken for entirely resolvable multipath returns in time-domain. The performance comparison is conducted particularly in a transition region, where multipath components are neither entirely resolvable nor highly clumped in time-domain. A qualitative analysis of the improvement in the target probability of detection with multipath exploitation is provided.

In this paper we deal with the performance prediction of the incoherent radar receiver in the presence of arbitrary correlated, possibly non-identically distributed target backscattered echoes. The problem is of interest in some common scenarios accounting for frequency agility, polarization as well as spatial diversity. We develop analytic expressions for the detection probability in terms of power series, assuming that a Generalized Swerling-Chi distribution models the first order probability density function (pdf) of the target amplitude. We also provide an interesting interpretation of the derived formulas. Finally, we assess the impact on the detection performance of both the correlation and the non-identical distribution of the target returns.

In this paper we consider the problem of Radar Specific Emitter Identification (SEI) with the aim of distinguishing among several transmitting sources of the same kind which is a very hot topic in the device forensic field. At the design stage, we introduce a classification technique based on some suitable features evaluated from the cumulants of the signal emitted by the radar system. The devised features share some invariance properties which make them very attractive for the SEI problem. Hence, we use them as the input to a K-Nearest Neighbor (KNN) classifier which performs the assignment of the emitter to a specific class. At the analysis stage, we assess the performance of the new system on a real dataset containing radar signals from three identical airborne emitters. The results highlight that a satisfactory classification performance is achieved.

A network of radars sharing the same frequency band, and using properly coded waveforms to improve features attractive from the radar point of view is considered in this article. Non-cooperative games aimed at code design for maximization of the signal-to-interference plus noise ratio (SINR) of each active radar are presented. Code update strategies are proposed, and, resorting to the theory of potential games, the existence of Nash equilibria is analytically proven. In particular, we propose non-cooperative code update procedures for the cases in which a matched filter, a minimum integrated sidelobe level filter, and a minimum peak to sidelobe level filter are used at the receiver. The case in which the received data contain a non-negligible Doppler shift is also analyzed. Experimental results confirm that the proposed procedures reach an equilibrium in few iterations, as well as that the SINR values at the equilibrium are largely superior to those in the case in which classical waveforms are used and no optimization of the radar code is performed. © 2013 Piezzo et al.; licensee Springer.

Design of Pareto-Optimal Radar Receive Filters This paper deals with the design of radar receive filters jointly optimized with respect to sidelobe energy and sidelobe peaks via Pareto-optimal theory. We prove that this criterion is tantamount to jointly minimizing two quadratic forms, so that the design can be analytically formulated in terms of a multi-objective optimization problem. In order to solve it, we resort to the scalarization technique, which reduces the vectorial problem into a scalar one using a Pareto weight defining the relative importance of the two objective functions. At the analysis stage, we assess the performance of the receive filters in correspondence of different values of the Pareto weight highlighting the performance compromises between the Integrated Sidelobe Level (ISL) and the Peak Sidelobe Level (PSL).

High resolution Ka-band radar data corresponding to small maritime targets and target-free sea-clutter acquisitions are used to evaluate the experimental performance of range distributed target coherent detectors. The statistical analysis of the sub-meter range resolution clutter data shows strong fluctuations of both the spectral shape and power between adjacent range cells. The assessment of the impact of this highly heterogeneous disturbance in the CFAR behavior of distributed target detectors is a key aim of this work. Additionally, the availability of coherent target data at a range resolution of 0.08m, 0.16m and 0.8m has been exploited to analyze the detection performance of both subspace and rank one detection approaches.

This study deals with the problem of Pareto-optimal waveform design in the presence of coloured Gaussian noise, under a similarity and an energy constraint. At the design stage, the authors, determine the optimal radar code according the following criterion: constrained maximisation of the detection performance and constrained minimisation of the Cramer-Rao lower bound (CRLB) on the Doppler estimation accuracy. This is tantamount to jointly maximising two quadratic forms under two quadratic constraints, so that the problem can be formulated in terms of a non-convex multi-objective optimisation problem. In order to solve it, the authors resort to the scalarisation technique, which reduces the vectorial problem into a scalar one using a Pareto weight defining the relative importance of the two objective functions. At the analysis stage, the authors assess the performance of the proposed waveform design scheme in terms of detection performance, region of achievable Doppler estimation accuracy and ambiguity function. In particular, the authors analyse the role of the Pareto weight in the optimisation process.

This study deals with the problem of detecting a radar target with unknown complex amplitude in the presence of clutter modelled as a spherically invariant random vector (SIRV). It is proved that, under a quite mild technical condition, the normalised matched filter (NMF) is the uniformly most powerful invariant (UMPI) detector in SIRV disturbance. The developed proof is based on the theory of total positive functions of order two.

In this paper, we deal with the problem of constrained code optimization for radar space-time adaptive processing (STAP) in the presence of colored Gaussian disturbance. At the design stage, we devise a code design algorithm complying with the following optimality criterion: maximization of the detection performance under a control on the regions of achievable values for the temporal and spatial Doppler estimation accuracy, and on the degree of similarity with a pre-fixed radar code. The resulting quadratic optimization problem is solved resorting to a convex relaxation that belongs to the semidefinite program (SDP) class. An optimal solution of the initial problem is then constructed through a suitable rank-one decomposition of an optimal solution of the relaxed one. At the analysis stage, we assess the performance of the new algorithm both on simulated data and on the standard challenging the Knowledge-Aided Sensor Signal Processing and Expert Reasoning (KASSPER) datacube.

This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of linearly coded pulse trains and determine the radar code which maximizes the detection performance under a control on the region of achievable Doppler estimation accuracies, and imposing a similarity constraint with a pre-fixed radar code. The resulting optimization problem is non-convex and quadratic. In order to solve it, we propose a technique based on the relaxation of the original problem into a semidefinite program. Indeed the best code is determined through a rank-one decomposition of an optimal solution of the relaxed problem. At the analysis stage we assess the performance of the new encoding technique in terms of detection performance, region of achievable Doppler estimation accuracies, and ambiguity function.

Sidelobe blanking (SLB) devices are used within radar systems to reduce the number of false alarms due to impulsive interference incoming through the sidelobes. The aim of this paper is to study the effects of different jamming techniques on a phased array radar equipped with a SLB device. The entire system is modeled accounting for the mutual coupling among the antenna array elements, and two different configurations for the position of the SLB element are considered. The cases of co-polarized and cross-polarized jamming signals are investigated. The analysis is focused on both the jammer capability to produce false targets and on its ability to mask the useful echo.

Modeling clutter signal is a major task in characterizing radar performances. Clutter distributions pertaining classical radar systems have been widely investigated, but in the past few years passive radars have raised interest among researchers for some specific features that make them very appealing in modern applications, thus bringing about the need for researching appropriate models for the noise signal received under no-target conditions. The paper proposes a new two- state model for the statistical distribution of clutter signal in a passive radar based on the GSM cellular system. A dedicated measurement station has been set up to carry out experiments in a real environment. Results give evidence of the suitability of the proposed model.

In this paper we consider the problem of adaptive radar detection in Gaussian disturbance with unknown spectral properties. To this end we resort to a Bayesian approach based on a suitable model for the probability density function of the unknown disturbance covariance matrix. We devise two detectors based on the GLRT criterion both one-step and two-step. The suggested decision rules ensure the same performance of the non Bayesian GLRT detectors when the size of the training set is sufficiently large. However they significantly outperform the counterparts in the presence of heterogeneous scenarios, where a small number of homogeneous training data is available. The analysis is also supported by results on high fidelity radar data from the KASSPER program. © 2007 IEEE.

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. A new detector based on the Rao test criterion is introduced and its invariance properties and constant false alarm rate (CFAR) behavior are studied. At the analysis stage, the performance of the new receiver is assessed, also in comparison with some classical adaptive radar detectors, both in the matched as well as in the mismatched signal case. Remarkably, the Rao test may achieve a matched detection performance which is commensurate with that of the generalized likelihood ratio test (GLRT)-based detectors if a sufficient number of training data is available. Moreover, it also exhibits better rejection capabilities of mismatched signals than the counterparts. In the last part of the work, a two-stage detector whose second stage coincides with the Rao test is devised. It represents a suitable means to restore the detection performance of the plain Rao test in the presence of a small number of training data. Finally, the performance of the aforementioned two-stage processor is analyzed in closed form and the CFAR behavior is proved.

In this paper, we deal with the problem of adaptive signal detection in colored Gaussian disturbance. Since the classical receivers may exhibit severe performance degradations in the presence of steering vector mismatches and sidelobe interfering signals, we try to account for the quoted drawbacks, very usual in realistic radar scenarios, at the design stage. To this end, we first characterize the set where the useful received signal may lie and its complement, i.e., the set which may contain the signals to be rejected. Then we resort to the generalized likelihood ratio (GLR) principle and devise detectors capable of operating in the presence of array response mismatches and sidelobe interfering signals. At the analysis stage, we assess the performance of the newly introduced receivers also in comparison with previously proposed detectors. The results show that the new processors are characterized by a wide range of performance compromises, selectable at the design stage through the regulation of a design parameter, between the detection of useful signals and the rejection of sidelobe interference.

In this paper we consider the issue of adaptive transmission and detection for MIMO radars operating under clutter with unknown covariance. In particular, we show that the availability of a set of secondary data allows defining constant-false-alarm rate (CFAR) receivers starting upon a family of previously known non-adaptive structures. We also show that, if the clutter correlation remains constant in several scans, an adaptive waveform selection procedure can also be implemented. The results show that the adaptive transmit/receive structures perform satisfactorily in comparison to their non-adaptive counterparts, the loss being in the order of 2-3 dB's for customary values of the system parameters.

In this paper we consider the synthesis of radar waveforms in the presence of colored disturbance. We focus on the class of coded pulse trains and determine the best radar codes according to some established optimality criteria. We propose three coding methods. Two of them are based on the maximization of the detection performance under a control on the estimation accuracy of the target Doppler frequency. The third relies on the maximization of the detection performance under a binary constraint on the code alphabet. Two proposed techniques require the solution of non-convex optimization problems which are solved resorting to the theory of relaxation and semidefinite programming. At the analysis stage the performances of the new coding strategies are assessed also in comparison with a standard radar code both in terms of detection probability and target Doppler estimation accuracy. The results show that the proposed techniques may lead to codes whose performances are close to the benchmark curves.

This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a priori information provided by a geographic information system, chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes is analysed in the presence of real radar data, collected by the McMaster IPIX radar, and compared with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a priori information available about the sensed environment.

This paper is focused on multichannel radar array processing and addresses some challenging issues concerning: the adaptation in nonstationary and nonhomogeneous environments, the value of Space-Time Adaptive Processing (STAP) in an operational High Frequency (HF) radar system, the benefits resulting from the joint use of multiple (not colocated) transmitters and receivers, and the parallel implementation of the multichannel processor. The work follows on where the importance of a multichannel processing is demonstrated with reference to airborne, ground-based, and Over The Horizon (OTH) radar systems.

This paper is concerned with adaptive Doppler processing in high frequency (HF) over-the-horizon (OTH) radar to improve the detection of slow-moving ships against clutter. A knowledge-based adaptive detection approach that exploits additional information to identify the surfaces that scatter clutter in each radar resolution cell (i.e. land, sea or land-plus-sea) is proposed to guide the selection of training data for the adaptive algorithm. The practical performance of the KB scheme is compared with conventional FFT-based Doppler processing and a traditional adaptive algorithm that does not exploit additional information.

In this paper we deal with the problem of adaptive signal detection in colored Gaussian disturbance. Since the classical receivers may exhibit severe performance degradations in the presence of steering vector mismatches and sidelobe interfering signals, we try to account for the quoted drawbacks, very usual in realistic radar scenarios, at the design stage. To this end we first characterize the set where the useful received signal may lie and its complement, i.e. the set which may contain the signals to be rejected. Then we resort to the Generalized Likelihood Ratio (GLR) principle and devise detectors capable of operating in the presence of array response mismatches and sidelobe interfering signals.