TR2016-057

A Recursive Born Approach to Nonlinear Inverse Scattering

    •  Kamilov, U., Liu, D., Mansour, H., Boufounos, P.T., "A Recursive Born Approach to Nonlinear Inverse Scattering", IEEE Signal Processing Letters, DOI: 10.1109/​LSP.2016.2579647, Vol. 23, No. 8, pp. 1052-1056, August 2016.
      BibTeX TR2016-057 PDF
      • @article{Kamilov2016jun,
      • author = {Kamilov, Ulugbek and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T.},
      • title = {A Recursive Born Approach to Nonlinear Inverse Scattering},
      • journal = {IEEE Signal Processing Letters},
      • year = 2016,
      • volume = 23,
      • number = 8,
      • pages = {1052--1056},
      • month = jun,
      • doi = {10.1109/LSP.2016.2579647},
      • url = {https://www.merl.com/publications/TR2016-057}
      • }
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  • Research Area:

    Computational Sensing

Abstract:

The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semitransparent objects. In this letter, we present a novel nonlinear inverse scattering method that combines IBA with an edgepreserving total variation (TV) regularizer. The proposed method is obtained by relating iterations of IBA to layers of an artificial multi-layer neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.

 

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