Sonar Echoic and Information Flow Field Processing and Learning For Interactive Sensing and Inference
Abstract
A critical need of the U.S. Navy is the development of a reliable, efficient and robust underwater target detection and classification system that coupled with interactivesensing and adaptive control for navigation can operate successfully in differentenvironmental and operating conditions. To this end, new methods are proposed to:(a) map sonar returns from targets embedded in clutter to time-varying rangeangle-Doppler detection maps using the adaptive coherence estimator (ACE)detection statistics and the corresponding distributions for sequential detection ofpotential targets,(b) use the ACE detection statistics to generate an information flow measure,(c) adaptively control the vehicle trajectory utilizing the information flow field forcontact interrogation,(d) use of information flow in the dynamical tracker, where control of the vehicleis used to maximize information flow,(e) utilize the ACE detection maps to further interrogate the sonar returns andproduce an echoic flow field by performing high resolution range-angle-Dopplerimaging, and(f) perform sequential target classification using the temporal evolution of theechoic flow field.We plan to demonstrate the usefulness of the developed methods on the synthesizedsonar data embedded in real clutter for a side-looking sonar (SLS) platform and theninvestigate its applicability to real sonar datasets especially those collected by a differentplatform such as forward-looking sonar (FLS). The results of this research will bebeneficial to many DoD areas involving autonomous sensing and inference.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 04, 2018
- Source ID
- N000141812805
Entities
People
- Mahmood Azimi-sadjadi
Organizations
- Colorado State University
- Office of Naval Research
- United States Navy