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).

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.

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.

In this paper we consider 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 first 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 new robust adaptive detectors resorting to the two-step generalized-likelihood ratio test (GLRT) design procedure without assignment of a distinct set of secondary data. Finally, a performance assessment, conducted by Monte Carlo simulation, show that the proposed detectors achieve a visible performance improvement over their natural counterparts.

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.