Elastic Target Modeling for Physics-Based Automatic Classification
Abstract
This document is a final report for MR-2649, focused on developing methods for relating the elastic physics of the targets of interest to the scattered returns and leveraging the spectral and temporal structure of those returns to improve automatic classification. The primary results presented in the report demonstrate two different approaches to relating observables in scattered returns to the underlying internal physics. First, we model the returns from a target manufactured for this project to break down the observed structures in the return and categorize those structures by the type of associated elastic behavior. We compare the modeled responses to the data collected in the ClutterEx17 experiment, both to validate the modeling and to understand the types of variation to which a feature set must be robust. Second, we analyze two existing classification systems used in studies involving simulated data and data collected in various underwater environments for the purpose of identifying the features currently being used, testing system robustness, and to identify opportunities for improvement. Finally, we consider the implications of the movement to a downlooking sonar in the development of feature sets and classification architectures. The overall assessment of the report is that the modeling approach can be successfully used to identify observables which can be understood in terms of the underlying physics and used as a basis for targeted feature sets. We describe how the results and conclusions obtained in this effort will direct upcoming follow-on work.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 30, 2020
- Accession Number
- AD1169176
Entities
People
- Aubrey Espana
- Lane Owsley
Organizations
- University of Washington