Convex relaxation methods: a review and application to sparse radar imaging of rotating targets
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2017
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Nguyen, N.H.
Dogancay, K.
Berry, P.E.
Tran, H.T.
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In this report we explore the use of sparse signal representation methods in the radar imaging problem of rotating targets and compare their results. The ultimate goal is to estimate the spatial locations and corresponding reflectivities of the scatterers constituting a target, based on a signal scattered from it. We pay particular attention to the so-called convex relaxation methods, which presumably can give the sparsest possible solutions and are computationally tractable while providing provable theoretical performance guarantees. We provide a comprehensive survey on various convex relaxation problem formulations known to date, as well as known computation alalgorithms for solving the optimization problems. By using extensive numerical simulations with simple rotating point targets, we show that, while many of these methods perform satisfactorily for 'on-grid' cases, performance for 'off-grid' cases is mostly unsatisfactory, warranting much further research before they can be efficiently applied to the inverse problem of radar imaging.
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Copyright 2017 Commonwealth of Australia