Approximate extraction of late-time returns via morphological component analysis.

Academic Article


  • A fundamental challenge in acoustic data processing is to separate a measured time series into relevant phenomenological components. A given measurement is typically assumed to be an additive mixture of myriad signals plus noise whose separation forms an ill-posed inverse problem. In the setting of sensing elastic objects using active sonar, we wish to separate the early-time return from the object's geometry from late-time returns caused by elastic or compressional wave coupling. Under the framework of morphological component analysis (MCA), we compare two separation models using the short-duration and long-duration responses as a proxy for early-time and late-time returns. Results are computed for a broadside response using Stanton's elastic cylinder model as well as on experimental data taken from an in-air circular synthetic aperture sonar system, whose separated time series are formed into imagery. We find that MCA can be used to separate early and late-time responses in both the analytic and experimental cases without the use of time-gating. The separation process is demonstrated to be compatible with image reconstruction. The best separation results are obtained with a flexible, but computationally intensive, frame based signal model, while a faster Fourier transform based method is shown to have competitive performance.
  • Authors

  • Goehle, Geoff
  • Cowen, Benjamin
  • Blanford, Thomas
  • Daniel Park, J
  • Brown, Daniel C
  • Status

    Publication Date

  • May 1, 2023
  • Has Subject Area

    Digital Object Identifier (doi)

    Pubmed Id

  • 37166339
  • Start Page

  • 2838
  • Volume

  • 153
  • Issue

  • 5