There is a good reason as to why these figures would be better detected with interferometry systems. One of the interesting feature of these constellations is that the human brain looks at them and figures they have a shape because they see three of more satellites as if they are always be forming a line. This is mainly an averaging effect but it is striking (Daniele can produce a constellation that reproduces the contour of a star, imagine that, a star looking at a star!) Since interferometric systems do make an average view (they have to collect enough light), we are also likely to see these lines in the interferometric data. And the best way to find them, will be use the curvelet transform. Since interferometric systems are also sparse, in term of light collection, it is likely that we will have incomplete data, Emmanuel Candes who has been working on curvelets, is also now working on incomplete fourier ensembles and he is aware of interferometric systems as witnessed in his recent paper entitled "Stable Signal Recovery from Incomplete and Inaccurate Measurements"
...Fourier ensemble. Suppose now that A is obtained by selecting p rows from the n×n discrete Fourier transform and renormalizing the columns so that they are unitnormed. If the rows are selected at random, the condition for Theorem 1 holds with overwhelming probability for S C · p/(log n)3 [4]. This case is of special interest as reconstructing a digital signal or image from incomplete Fourier data is an important inverse problem with applications in biomedical imaging (MRI and tomography), Astrophysics (interferometric imaging), and geophysical exploration.
It looks like, it is just a matter of time before we go out on a search for a big lone star, you know the five legged one...
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