Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions

The probability density function of the acoustic field amplitude scattered by the seafloor was measured in a rocky environment off the coast of Norway using a synthetic aperture sonar system, and is reported here in terms of the probability of false alarm. Interpretation of the measurements focused on finding appropriate class of statistical models (single versus two-component mixture models), and on appropriate models within these two classes. It was found that two-component mixture models performed better than single models. The two mixture models that performed the best (and had a basis in the physics of scattering) were a mixture between two K distributions, and a mixture between a Rayleigh and generalized Pareto distribution. Bayes' theorem was used to estimate the probability density function of the mixture model parameters. It was found that the K-K mixture exhibits significant correlation between its parameters. The mixture between the Rayleigh and generalized Pareto distributions also had significant parameter correlation, but also contained multiple modes. We conclude that the mixture between two K distributions is the most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical Society of Americ

Wideband synthetic aperture sonar backprojection with maximization of wave number domain support

Wideband and widebeam synthetic aperture sonar (SAS) can provide information on the frequency- and aspect-dependent scattering in a scene. We suggest an approach to predict the quality of the sensor data over the available frequencies and aspect angles. We relate the typical spatial domain quality metrics to their wave number domain (WD) counterpart, and use these to map the data quality in WD. Because SAS arrays often are undersampled along-track, we pay particular attention to data degradation from aliasing. We use the proposed approach to examine how three SAS image formation algorithms based on time domain backprojection (TDBP) access data of different quality from wideband SAS systems. We illustrate the results with predictions for a generic SAS design and demonstrate the findings on two experimental systems. We observe that the maximum support of high-quality data is achieved through BP onto a high-resolution grid followed by WD filtering.<br/

Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distribution

The article of record as published may be found at https://doi.org/10.1121/1.5089892The probability density function of the acoustic field amplitude scattered by the seafloor was measured in a rocky environment off the coast of Norway using a synthetic aperture sonar system, and is reported here in terms of the probability of false alarm. Interpretation of the measurements focused on finding the appropriate class of statistical models (single versus two-component mixture models), and on appropriate models within these two classes. It was found that two-component mixture models performed better than single models. The two mixture models that performed the best (and had a basis in the physics of scattering) were a mixture between two K distributions, and a mixture between a Rayleigh and generalized Pareto distribution. Bayes’ theorem was used to estimate the probability density function of the mixture model parameters. It was found that the K-K mixture exhibits a significant correlation between its parameters. The mixture between the Rayleigh and generalized Pareto distributions also had a significant parameter correlation, but also contained multiple modes. It is concluded that the mixture between two K distributions is the most applicable to this dataset.This work was supported by the U.S. Office of Naval Research under Grant Nos. N00014-18-WX00776, N00014-16-1-2335, N00014-13-1-0056, and N00014-12-1-0546.This work was supported by the U.S. Office of Naval Research under Grant Nos. N00014-18-WX00776, N00014-16-1-2335, N00014-13-1-0056, and N00014-12-1-0546