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

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.

Measurements and models of the correlation of redundant spatial coherence measurements for the incoherently scattered field.

Sonar systems that exploit correlation for navigation, such as correlation velocity logs and micronavigation for synthetic aperture sonar, often make redundant estimates of the spatial coherence of the scattered field at several spatial lags. Two models for the correlation of these redundant measurements are described. First, an analytical model is derived using the assumption of stationary Gaussian statistics. Next, a numerical model is described that accounts for non-stationary processes present in measurements of seafloor scattering. These models are compared to normal-incidence scattering data collected at Seneca Lake, NY. Both models show good agreement with the measurements when the spatial separation between redundant hydrophone pairs is less than the coherence length. At greater spatial separation, the analytical model diverges from the measurements. This disagreement is explained by a lack of stationarity in the measured data which is captured by the numerical model. Finally, spatial variations in the volume scattering strength of the sediment are identified as a source of the non-stationarity in the measurements.

Combining range-general and range-specific techniques for late-time focused image reconstructiona).

Synthetic aperture sonar (SAS) is an acoustic method for detecting objects in an environment. Conventional SAS image reconstruction techniques invert a forward model based on geometric scattering and straight-line propagation. Acoustic features that do not fit this model, such as multiple scattering and late-time returns, appear out of focus. This paper describes an image reconstruction technique that selectively applies range-general and range-specific methods to improve the focus of late-time returns while maintaining image quality away from the focal plane. The technique is demonstrated on experimental data and compared with a range-specific algorithm.

Late return focusing algorithm for circular synthetic aperture sonar data.

Synthetic aperture sonar processing is used to generate imagery for remote sensing applications such as environmental characterization and object detection. Images primarily represent the initial geometric response of acoustic scattering, but there are additional information embedded in the raw data that are not well-represented in images. For example, responses such as internal multiple scattering or elastic scattering are delayed in time, and they appear defocused in imagery. A complementary processing algorithm is presented that improves the focus of late acoustic scattering responses, which can potentially provide additional information about the object and aid data interpretability.