Maximum Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters

Wang, Jian; Dogandzic, A.; Nehorai, Arye

doi:10.1109/tsp.2006.880209

Cited by 97 publications

(66 citation statements)

References 23 publications

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“…To improve the robustness of the calibration, we propose, in this paper, a new scheme based on a broader class of distributions gathered under the socalled spherically invariant random noise modeling [37,38], which includes the Student's t distribution. A spherically invariant random vector (SIRV) is described as the product of a positive random variable, named texture, and the socalled speckle component which is Gaussian, resulting in a two-scale compound Gaussian distribution [39]. The flexibility of the SIRV modeling allows to consider non-Gaussian heavytailed distributed noise in the presence of outliers, but also to adaptively consider Gaussian noise in the extreme case when there are no outliers.…”

Section: Introductionmentioning

confidence: 99%

Robust Calibration of Radio Interferometers in Non-Gaussian Environment

Ollier

Korso²,

Boyer

et al. 2017

IEEE Trans. Signal Process.

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Abstract-The development of new phased array systems in radio astronomy, as the low frequency array (LOFAR) and the square kilometre array (SKA), formed of a large number of small and flexible elementary antennas, has led to significant challenges. Among them, model calibration is a crucial step in order to provide accurate and thus meaningful images and requires the estimation of all the perturbation effects introduced along the signal propagation path, for a specific source direction and antenna position. Usually, it is common to perform model calibration using the a priori knowledge regarding a small number of known strong calibrator sources but under the assumption of Gaussianity of the noise. Nevertheless, observations in the context of radio astronomy are known to be affected by the presence of outliers which are due to several causes, e.g., weak noncalibrator sources or man made radio frequency interferences. Consequently, the classical Gaussian noise assumption is violated leading to severe degradation in performances. In order to take into account the outlier effects, we assume that the noise follows a spherically invariant random distribution. Based on this modeling, a robust calibration algorithm is presented in this paper. More precisely, this new scheme is based on the design of an iterative relaxed concentrated maximum likelihood estimation procedure which allows to obtain closed-form expressions for the unknown parameters with a reasonable computational cost. This is of importance as the number of estimated parameters depends on the number of antenna elements, which is large for the new generation of radio interferometers. Numerical simulations reveal that the proposed algorithm outperforms the state-of-theart calibration techniques.

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Section: Introductionmentioning

confidence: 99%

Robust Calibration of Radio Interferometers in Non-Gaussian Environment

Ollier

Korso²,

Boyer

et al. 2017

IEEE Trans. Signal Process.

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“…We focus on the multivariate complex t-distribution, since it has long been recognized by several authors from both the statistical (see, e.g., [6] and the references therein) and the signal processing communities (see, e.g., [10][11][12][13]) as a suitable and flexible model able to describe the heavy-tailed behavior of the measurements in many practical applications (e.g., radar detection).…”

Section: Introductionmentioning

confidence: 99%

Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data

Fortunati

Gini

Greco

2016

EURASIP J. Adv. Signal Process.

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Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.

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“…In the radar community too, CG is known to be an accurate statistical model for the wavelet distribution of SAR images [5] with applications such as image reconstruction [6], and also forms the basis of the K-distribution [7][8] that is frequently used for modeling RCS returns from sea clutter data. Indeed CG is a very versatile statistical model that specializes to many well known distributions such as the α-stable and symmetrized Gamma distributions [1] and which serve as useful prototypes of heavy-tailed processes-thus providing the probabilistic underpinnings for the generation of various sparse-structured stochastic phenomena such as described above.…”

Section: Introductionmentioning

confidence: 99%