Stefan J. Wijnholds

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In radio astronomy, cosmic sources are observed which are many orders of magnitude weaker than the telescope system noise level. The necessary sensitivity is achieved by large telescope collecting areas, long integration times, and large bandwidths. In the coming two decades, telescopes are planned which are even one to two orders of magnitude more(More)
—The fidelity of radio astronomical images is generally assessed by practical experience, i.e. using rules of thumb, although some aspects and cases have been treated rigorously. In this paper we present a mathematical framework capable of describing the fundamental limits of radio astronomical imaging problems. Although the data model assumes a single(More)
Radio astronomy is known for its very large telescope dishes, but is currently making a transition towards the use of large numbers of small elements. For example, the Low Frequency Array, commissioned in 2010, uses about 50 stations, each consisting of at least 96 low band antennas and 768 high band antennas. For the Square Kilometre Array, planned for(More)
Image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user specified constraints to the reconstruction process that may produce an undesirable bias to the solution. We propose a data driven model based least squares reconstruction method based(More)
—The problem of estimating the direction-independent gain and phase characteristics of sensor arrays requires a boundary condition to solve the phase ambiguity of the solution. It has become common practice to use the constraint that the phase of the first element of the array is zero. By Cramér–Rao lower bound (CRLB) analysis, we show analytically for(More)
Current and future radio telescopes, in particular the Square Kilometre Array (SKA), are envisaged to produce large images (> 10 8 pixels) with over 60 dB dynamic range. This poses a number of image reconstruction and technological challenges, which will require novel approaches to image reconstruction and design of data processing systems. In this paper,(More)
Many imaging arrays have a regular sensor configuration. This regularity can be exploited for self-calibration of the array. In this paper, we introduce a new self-calibration method for regular arrays based on weighted alternating least squares (WALS) optimization that appears to be statistically efficient and does not impose requirements on the source(More)