This paper proposes a simple Constant False Alarm Rate (CFAR) approach relying on the expectation-maximization algorithm to deal with clutter edges. The possible signal backscattered from a target is modeled as an incoherent pulse train and the newly-introduced CFAR technique is assessed in conjunction with an energy detector. Natural competitors are the well-known Cell-Averaging (CA), Greatest Of (GO), and Smallest Of (SO) CFAR techniques. The performance analysis shows that the proposed approach outperforms the state-of-the-art competitors in terms of false alarms control while maintaining satisfying detection performance.

In remote sensing, target detection in hyperspectral systems is a crucial duty since it enables the localization and discrimination of target features. For this purpose, reflectance spectra are frequently utilized, and the spectral signatures with corresponding component abundances in the observed scene are displayed. Nevertheless, many hyperspectral sensors have restricted spatial resolution, namely only a part of the pixel is occupied by the targets, and the spectra of multiple sub-pixel targets, along with the background spectrum, gets combined within a single pixel. Therefore, we propose in this paper a generalized replacement model that considers different sub-pixel target spectra and execute the detection process as a binary hypothesis test. This method shows to work well in handling this problem.

In this paper, the problem of classifying radar clutter returns into statistically homogeneous subsets is addressed. To this end, latent variables, which represent the classes to which the tested range cells belong, in conjunction with the expectation- maximization method are jointly exploited to devise the classification architecture. Moreover, two different models for the structure of the clutter covariance matrix are considered. At the analysis stage, numerical examples based on simulated data for the classification performance are presented showing the effectiveness of the proposed classification schemes.

This paper proposes a decision scheme based on the$k$-nearest neighbors rule to detect coherent signals in non-Gaussian noise modeled as the sum of K-distributed clutter plus thermal noise. The analysis is conducted also on real data recordings and shows that the proposed detector can outperform natural competitors.

The problem of robust radar detection is addressed from a machine learning inspired perspective. In particular, a novel interpretation of the well-known Kelly's and adaptive matched filter (AMF) detectors is provided in terms of decision region boundaries in a suitable feature space. Then, a new detector based on a feature vector that combines the two detection statistics is obtained by exploiting the k-nearest neighbors (KNN) approach. The resulting receiver possesses the constant false alarm rate (CFAR) property and can achieve the same benchmark performance of Kelly's detector under matched conditions while being almost as robust as the AMF (which instead experiences a loss under matched conditions).

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference-plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.

A novel maximum likelihood trajectory estimation algorithm for targets in mixed stationary/moving conditions is presented. The proposed approach is able to estimate position and velocity of the target over arbitrary complex trajectories, while explicitly taking into account the possibility of stop&go motion. Moreover, a novel trajectory reconstruction method based on the theory of Bézier curve is developed for online smoothing of the trajectory, which keeps the advantages of Bayesian smoothing while introducing only a fixed lag in the estimation process. The performance assessment, conducted on both simulated and real data, shows that the proposed approach can outperform classical Kalman filter and Rauch-Tung-Striebel smoother techniques.

This letter addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the “position” of the target within the CPI and eventually detect it. The performance analysis shows that the newly proposed architectures can provide an accurate estimate of the target position along with improved detection performance with respect to existing competitors.

The limited localization capabilities provided by global navigation satellite systems (GNSS) is one of the main obstacles toward the development of reliable road safety applications in urban scenarios. In order to improve GNSS accuracy, a number of approaches have been proposed which exploit additional position-related information, for instance provided by local inertial sensors. However, such solutions cannot meet the very stringent accuracy requirements of safety applications, which call for advanced processing and the fusion of position-related signals and data from heterogeneous sources. In this paper, we aim at combining the potential of antenna array processing with a suitably-designed cooperation strategy that exploits vehicle-to-vehicle and vehicle-to-infrastructure communications. Particularly, we define a novel tracking algorithm with asynchronous updates triggered by beacon packet receptions, from which angle of arrival estimates are opportunistically obtained. A dynamic setting of relevant parameters allows the resulting cooperative positioning algorithm to adapt to the different operating conditions found in urban vehicular contexts. Simulation results under realistic environment conditions show that the proposed algorithm can achieve high position accuracy even in sparse scenarios, outperforming a natural competitor while keeping lightweight communication and low computational complexity.

Motivated by safety applications in urban vehicular scenarios, where GPS does not typically provide the required positioning accuracy, a GPS-free localization technique that exploits vehicle-to-infrastructure communications is proposed. In particular, it provides for a vehicle to opportunistically use the beacon packets received from a roadside unit (RSU) in order to obtain estimates of their angle of arrival. Such estimates, together with the RSU's position information within beacon packets, are fed to a weighted least squares algorithm that aims at localizing the vehicle. The algorithm tries to take advantage of reliable measurements typically collected closer to the RSU-where a very high signal-to-noise ratio yields an accurate angular resolution-while keeping robustness against multipath phenomena. Simulation results show the effectiveness of the proposed technique.

This paper deals with estimation of gain and phase errors possibly present in frequency modulated continuous wave radars. In particular, the Cramér-Rao lower bound of unbiased estimators of gain and phase errors in presence of nuisance parameters is computed. It is used as a reference for the performance of already proposed estimators (computed by Monte Carlo simulation).

Adaptive radar detection of point-like targets in presence of disturbance with unknown spectral properties is addressed. By relaxing the assumptions of the W-ABORT, a tunable detector with improved detection vs rejection trade-off is proposed. To this aim, the presence of a fictitious signal under the null hypothesis is modeled probabilistically, so that an additional degree of freedom is introduced in the statistic of the detector. The resulting parametric GLRT shows a range of possible behaviors, from Kelly's detector to the plain W-ABORT. Monte Carlo simulations reveal that an improved compromise can be obtained. Simple closed-form approximations are also given with near-optimal performance, so that the ultimate computational complexity remains comparable to that of the W-ABORT.

This paper proposes a procedure based on time-difference of arrival measurements to localize a blind node in an asynchronous network where a set of nodes with known position is present. The proposed method computes the time-difference of arrival of each transmitted signal to any pair of receiving nodes in order to get rid of the unknown transmission time. A range-based localization procedure is implemented: first a least-squares estimator is used to compute a set of pseudo-ranges involving the blind node, then an iterative least-squares method is used to localize the blind node. The effectiveness of the proposed scheme is illustrated via simulations.

In this paper, we deal with the problem of RSS-based self-localization of a wireless blind node using a statistical path loss model for the measurements. The considered environment is non-homogeneous, i.e., the attenuation factors of the various links are different. We propose a two-stage procedure: the first stage exploits measurements between anchors to estimate transmitted powers and attenuation factors. Then, a ML localization algorithm, fed by the measurements at the blind node only, is used to estimate the unknown position. In this second stage, the attenuation factors between the blind node and the anchors are modeled as IID RVs ruled by a Gaussian distribution with mean and variance to be computed based on the estimated attenuation factors of the first stage. The performance assessment shows that the proposed approach could be a viable means to handle localization in non-homogeneous environments.

In this paper, we use the tools of statistical hypothesis testing to determine whether or not the different links of a WSN are homogeneous. At the design stage we use a statistical path loss law to model the RSS measurements. More precisely, in the homogeneous case all links share one and the same attenuation factor while in the non-homogeneous one the attenuation factors of the various links are different. We thus derive a GLRT-based decision rule for the considered problem and compute its distribution. Some numerical examples are finally presented to evaluate the potential to discriminate between the two hypotheses.

We propose a new approach to adaptive detection of coherent signals backscattered by possible point-like targets in the context of electronic warfare; in fact, the possible target signal is buried in thermal noise, clutter, noise-like interferers and, possibly, coherent (i.e., deceptive ECM) interferers. We assume a network of radars: for a given cell under test only a subset of the radars receives ECM signals. Training data containing thermal noise, clutter, and noise-like interferers are available. The problem at hand is solved resorting to a two-stage detection strategy: first, the subset of radars under ECM is estimated; then, a proper detection strategy resorting to W-ABORT based detectors for radars under ECM is implemented. The performance assessment shows that the proposed solution is effective in presence of ECM systems.

This paper proposes a Bayesian edge detector to be fed by polarimetric, possibly multifrequency, synthetic aperture radar (SAR) data. It can be used to detect dark spots on the ocean surface and, hence, as the first stage of a system for identification and monitoring of oil slicks. The proposed detector does not require secondary data (i.e., pixels from a slick-free area) but for a certain a priori knowledge; remarkably, a preliminary performance assessment, based on both synthetic and real SAR recordings, shows that it has a slightly better performance in terms of detection and false alarm control than previously proposed classical (i.e., non-Bayesian) detectors.

We address the problem of estimating a covariance matrix R using K samples zkwhose covariance matrices are τkR, where τkare random variables. This problem naturally arises in radar applications in the case of compound-Gaussian clutter. In contrast to the conventional approach which consists in considering R as a deterministic quantity, a knowledge-aided (KA) approach is advocated here, where R is assumed to be a random matrix with some prior distribution. The posterior distribution of R is derived. Since it does not lead to a closed-form expression for the minimum mean-square error (MMSE) estimate of R, both R and τkare estimated using a Gibbs-sampling strategy. The maximum a posteriori (MAP) estimator ofR is also derived. It is shown that it obeys an implicit equation which can be solved through an iterative procedure, similarly to the case of deterministic τks, except that KA is now introduced in the iterative scheme. The new estimators are shown to improve over conventional estimators, especially in small sample support.

We propose a two-stage detector consisting of a subspace detector followed by the whitened adaptive beamformer orthogonal rejection test. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown co-variance matrix of the noise and that it guarantees a wider range of directivity values with respect to previously proposed two-stage detectors. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been evaluated by means of numerical integration techniques.

In this paper we propose decision schemes to distinguish between the H0hypothesis that range cells under test contain disturbance only (i.e., noise plus interference) and the H1hypothesis that they also contain signal components along a direction which is a priori unknown, but constrained to belong to a given subspace (H) of the observables. The disturbance is modeled in terms of complex normal noise vectors plus deterministic interference assumed to belong to a known subspace (J) of the observables. At the design stage we resort to either the plain generalized likelihood ratio test (GLRT) or the two-step GLRT-based design procedure. Moreover, we assume that a set of noise only (secondary) data is available. A preliminary performance analysis, conducted by resorting to simulated data, shows that the one-step GLRT performs better than the two-step GLRT-based design procedure

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 design and assess adaptive schemes to detect extended and multiple point-like targets embedded in correlated Gaussian noise. Proposed algorithms rely on either the generalized likelihood ratio test (GLRT) or ad hoc procedures. Such detectors make it possible to get rid of distinct secondary data and guarantee the constant false alarm rate (CFAR) property with respect to the covariance matrix of the disturbance. A preliminary performance assessment, conducted by resorting to simulated data, also in comparison to the so-called modified GLRT (MGLRT) proposed in , has shown that newly introduced CFAR detectors may represent a viable means to deal with uncertain scenarios.

This letter proposes a generalized-likelihood ratio test-based edge detector to be fed by possibly polarimetric synthetic aperture radar (SAR) data. It can be used to detect dark spots on the sea surface and, hence, as the first stage of a system for identification and monitoring of oil spills. The proposed constant false alarm rate (CFAR) detector does not require secondary data (namely pixels from a slick-free area); remarkably, a preliminary performance assessment, carried out by resorting to real SAR recordings, shows that it guarantees detection capabilities comparable to those of previously proposed polarimetric CFAR detectors (which though make use of secondary data). The preliminary performance assessment also seems to indicate that processing polarimetric data does not ensure improved detection capabilities.

This paper addresses blind multiuser detection in a direct-sequence code-division multiple-access (DS-CDMA) network in presence of both multiple-access interference and intersymbol interference. In particular, it considers a DS-CDMA system where K out of N users are transmitting; the N admissible spreading codes are known, and so is the code of the user to be demodulated. The number of interferers, the signatures of a certain number, possibly all, of the interferers, and the channel impulse response of each active user are unknown. The spreading codes of the unknown interferers are determined via a procedure that exploits the knowledge of the set of admissible transmitted codes and of the known active codes. The procedure applies to both single and multiple receiving antennas. The performance assessment of a blind decorrelating detector, implemented by resorting to the proposed identification procedure, shows that it outperforms a plain subspace-based blind decorrelator for small sizes of the estimation sample.

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.

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.

This paper handles the simultaneous suppression of narrow-band and multiaccess interference in code division multiple-access (CDMA) direct-sequence spread-spectrum (DSSS) systems. The basic structure we refer to is reminiscent of the decorrelating detector, but here the design strategy relies on the concept of combating jointly the two interference sources-precisely, a decision as to the bit transmitted by each user is made based on the projection of the observables onto the orthogonal complement to the subspace spanned by the other users' signatures and the narrow-band interference. We focus on several different implementations of such a strategy, assuming a different degree of prior knowledge as to the narrow-band interference. An important side result of the proposed approach is that, in general, complete suppression of data-like interference may be achieved through periodically time-varying processing. An adaptive version of such a receiver is also presented, wherein the projection direction is estimated based on suitable estimates of the covariance properties of the observables. The value of this method is also assessed by studying the rate of convergence of the estimated direction to the true projection direction.

The article addresses radar detection of coherent pulse trains embedded in spherically invariant noise with unknown statistics. Starting upon a newly proposed detector, which assumes knowledge of the structure of the clutter covariance matrix, we substitute the actual matrix by a proper estimate based on a set of secondary data vectors. Interestingly, the resulting detector achieves a constant false alarm rate with respect to the texture component of the clutter, and incurs an acceptable loss with respect to the case of a known covariance matrix.