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This paper deals with estimation of gain and phase errors possibly present in frequency modulated continuous wave radars. In particular, the Cramer–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).

Direct antivirals are available for treating recurrent hepatitis C (RHC). This study reported outcomes of 424 patients with METAVIR F3–F4 RHC who were treated for 24 weeks with sofosbuvir/ribavirin and followed for 12 weeks within the Italian sofosbuvir compassionate use program. In 55 patients, daclatasvir or simeprevir were added. Child–Pugh class and model of end stage liver disease (MELD) scores were evaluated at baseline and 36 weeks after the start of therapy. The sustained viral response (SVR) was 86.7% (316/365) in patients who received sofosbuvir/ribavirin and 98.3% (58/59) in patients who received a second antiviral (P < 0.01). In patients treated with sofosbuvir/ribavirin, a significant difference in SVR was observed between patients diagnosed with METAVIR F4 (211/250; 84.4%), METAVIR F3 (95/105; 90.5%) and fibrosing cholestatic hepatitis (10/10; 100%) (P = 0.049). A significant association was found between patients who worsened from Child–Pugh class A and who experienced viral relapse (4/26 vs. 8/189, P = 0.02). In patients with a baseline MELD score

In this correspondence, we focus on the design and the analysis of schemes aimed at estimating the position of multiple point-like targets that fall among three adjacent samples of the matched filter output (typically present in the processing chain of a radar). To this end, we exploit spillover of targets’ energy to adjacent range cells. The number of targets (and the corresponding Doppler frequency shifts) is assumed to be known. Moreover, we suppose that received useful signals can be modeled in terms of vectors known up to deterministic parameters and that they are embedded in correlated Gaussian noise with unknown covariance matrix. For estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, is available. The analysis, also in comparison to a possible competitor, proves the superiority of multitarget schemes with respect to single target ones, even under reasonably mismatched scenarios.

This paper addresses the problem of detecting multiple point-like targets in the presence of steering vector mismatches and Gaussian disturbance with unknown covariance matrix. To this end, we flrst model the actual useful signal as a vector belonging to a proper cone whose axis coincides with the whitened direction of the nominal array response. Then we develop two robust adaptive detectors resort- ing to the two-step GLRT-based design procedure without assignment of a distinct set of secondary data. The performance assessment has been conducted by Monte Carlo simulation, also in comparison to pre- viously proposed detectors, and conflrms the efiectiveness of the newly proposed ones. In the last part of the work, in order to restore the detection performance of the newly proposed detectors in the presence of a large number of range cells contaminated by useful signals, we consider two adaptive detectors which resort to the structure informa- tion of the disturbance covariance matrix, and show that the a-priori information on the covariance structure can lead to a noticeable per- formance improvement.

We address adaptive detection of coherent signals backscattered by possible point-like targets or originated from electronic countermeasure (ECM) systems in presence of thermal noise, clutter, and possible noise-like interferers. In order to come up with a class of decision schemes capable of discriminating between targets and ECM signals, we resort to generalized likelihood ratio test (GLRT) implementations of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test. The performance assessment focuses on an adaptive beamformer orthogonal rejection test (ABORT)-like detector; analytical expressions for the probability of false alarm, the probability of detection of the target, and the probability of blanking the ECM (coherent) signal are given. More remarkably, it guarantees the constant false alarm rate (CFAR) property. The performance assessment shows that it can outperform the adaptive sidelobe blanker (ASB) in presence of ECM systems.

In this paper we deal with the problem of adaptive detection of mismatched mainlobe targets and/or sidelobe interfering signals that are distributed in range. To this end, we investigate the impact of modeling the actual useful signal as a vector belonging to a proper cone with axis the nominal steering vector as a means to improve the robustness of the decision rule in presence of mainlobe targets; similarly, in order to improve the rejection capabilities of the decision rule in presence of sidelobe interferers we study the effects of replacing the usual noise-only hypothesis with a noise-plus-interferers hypothesis where interferers belong to the complement of a cone with axis the nominal steering vector. At the design stage we resort to the two-step GLRT-based design procedure; to this end, we assume that a set of training data is available, namely data free of signal components, but sharing the same Gaussian distribution of the noise in the cells under test. Remarkably, proposed detectors possess the CFAR property under the noise-only hypothesis. The performance assessment, conducted by Monte Carlo simulation, is aimed at assessing the effectiveness of proposed solutions, also in comparison to existing ones.

We propose a modified version of the adaptive sidelobe blanker (ASB) consisting of a generalized likelihood ratio test (GLRT)-based subspace detector followed by the adaptive coherence estimator. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise in homogeneous environment and that it guarantees a wider range of ldquodirectivityrdquo values with respect to the plain ASB. The probability of false alarm and the probability of detection (the latter for matched signals only) have been evaluated in closed form in homogeneous environment and by resorting to Monte Carlo simulation for the other considered cases.

We present an adaptive algorithm aimed at detecting multiple point-like radar targets embedded in correlated Gaussian noise. The proposed detector modifies and improves the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of multiple targets. More precisely, it relies on the so-called two-step generalized likelihood ratio test (GLRT) design procedure implemented without assignment of a distinct set of secondary data. The newly proposed detector can guarantee the constant false alarm rate (CFAR) property and the performance assessment, conducted resorting to simulated data, has shown that it exhibits better rejection capabilities of mismatched signals than previously proposed detectors, at the price of an acceptable performance loss for matched signals

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 [5], has shown that newly introduced CFAR detectors may represent a viable means to deal with uncertain scenarios.

In this paper, we deal with the problem of adaptive radar detection of point-like targets in presence of noise with unknown spectral properties. As customary, we assume that a set of data sharing the same properties of the noise in the cell under test is available. A Bayesian framework is adopted at the design stage. More precisely, the noise is assumed conditionally complex normal, given the covariance matrix, that in turn is ruled by a complex inverse Wishart distribution. In addition, in order to come up with detectors with good rejection capabilities, the possible presence of a fictitious signal under the null hypothesis is modeled probabilistically, as opposite to the conventional ABORT-like approach. Two detectors are proposed, which reveal a good trade-off between detection power and selectivity even assuming a limited number of training data.

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 homogeneous, i.e., the attenuation factors of the various links are one and the same while the transmitted powers are different. The blind node is moving along a trajectory that is unknown. We propose a two-stage procedure: the first stage exploits measurements between anchors, i.e., nodes of known position, to compute the ML estimate of the transmitted powers and the attenuation factor. Then, a ML solution, using measurements at the blind node only, is proposed to estimate the unknown trajectory, based upon the Viterbi algorithm. The performance assessment, carried out also in comparison to other algorithms, shows that the proposed approach could be a viable means to handle localization of moving nodes in uncertain scenarios.

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.