14 May 2016 Moving target imaging using sparse and low-rank structure
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In this paper we present a method for passive radar detection of ground moving targets using sparsely distributed apertures. We assume the scene is illuminated by a source of opportunity and measure the backscattered signal. We correlate measurements from two different receivers, then form a linear forward model that operates on a rank one, positive semi-definite (PSD) operator, formed by taking the tensor product of the phase-space reflectivity function with its self. Utilizing this structure, image formation and velocity estimation are defined in a constrained optimization framework. Additionally, image formation and velocity estimation are formulated as separate optimization problems, this results in computational savings. Position estimation is posed as a rank one PSD constrained least squares problem. Then, velocity estimation is performed as a cardinality constrained least squares problem, solved using a greedy algorithm. We demonstrate the performance of our method with numerical simulations, demonstrate improvement over back-projection imaging, and evaluate the effect of spatial diversity.
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Eric Mason, Eric Mason, Birsen Yazici, Birsen Yazici, } "Moving target imaging using sparse and low-rank structure", Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430D (14 May 2016); doi: 10.1117/12.2224363; https://doi.org/10.1117/12.2224363


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