Approximate extraction of late-time returns via morphological component analysis
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 01, 2023
- Source ID
- 10.1121/10.0019415
Entities
People
- Benjamin Cowen
- Daniel C Brown
- Geoff Goehle
- J. Daniel Park
- Thomas E. Blanford
Organizations
- Office of Naval Research
- Pennsylvania State University