Sparsity and Compressed Sensing in Radar Imaging

  title={Sparsity and Compressed Sensing in Radar Imaging},
  author={Lee C. Potter and Emre Ertin and Jason T. Parker and M{\"u}jdat Çetin},
  journal={Proceedings of the IEEE},
Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear reconstruction algorithms and pseudorandom linear measurements to provide reconstruction guarantees for sparse solutions to linear inverse problems… CONTINUE READING
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