Ahmad Mouri Sardarabadi

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Aims. Image formation for radio astronomy can be defined as estimating the spatial intensity distribution of celestial sources throughout the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a priori(More)
Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is known. If the noise covariance matrix is unknown, training or other calibration techniques are used to find it. In this paper another approach to the problem of unknown noise covariance is presented. The complex factor analysis(More)
As the number of antennas in the modern radio-telescopes increases, the computational complexity of the calibration algorithms becomes more and more important. In this paper we use the Khatri-Rao structure of the covariance data model used for such calibrations and combine it with Krylov subspace based methods to achieve accurate calibration results with(More)
Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one(More)
A simple and novel algorithm for source recovery based on array data measurements in radio astronomy is proposed. Considering that a radioastronomical image is composed of both point sources and extended emissions, prior information on the images, namely non-negativity and substantial black background are taken into account to choose source representation(More)
We show that the imaging problem for radio astronomy is not only bounded below (the image is non-negative), but there is also an upper bound. We show that the tightest upper bound is the MVDR dirty image. We propose using active-set methods to solve the imaging problem and show that this algorithm is strongly related to sequential source removal techniques(More)
Linear image deconvolution for radio-astronomy is an ill-posed problem. For this reason, a-priori knowledge is crucial for improving the performance of the deconvolution. In this paper we show that combining non-negativity constraints with an upper bound on the magnitude of each pixel in the image can significantly improve the image formation algorithm. We(More)
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