Leveraging manifold signal processing and information theory to enable efficient feature engineering for autonomous sonar target detection and classification
Abstract
Approved for public releaseWe propose a twenty-eight month basic research effort to develop a robust physics-cognizant mathematicalframework to represent and differentiate different classes of acoustic echoes from sonar targets. Autonomous identification of sonar targets is challenging due to the rapid fluctuations of the target scattering signature, as well as the lack of coherence between pings. We formulate the sonar target identification problem fundamentally as a feature engineering challenge, and focus on designingfeature extraction techniques that can characterize and identify a sonar target based on the minimum (or close to minimum) number of pings. Specifically, we design manifold signal processing techniques that extract target information implicitly contained in each ping, and study how that information varies from ping to ping, and get corrupted by background noise or interference from clutter. In particular, we harness robust theoretical underpinnings in information anddetection theory with the idea of braid manifolds to address this.We validate our methods on acoustic echoes from sonar targets represented by experimental field data from the Malta experiment, such as reflected acoustic echoes from an oil tanker, rig, etc. To clearly scope our efforts in this proposed project, we focus on acoustic echoes and related sonar signal processing that are distinct from computational techniques designed for shorter-range smaller targets such as proud/buried underwater mines, etc. where inter-ping coherence maybe significant and the scattering models are fundamentally different.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 05, 2021
- Source ID
- N000142112420
Entities
People
- Ananya Sen Gupta
Organizations
- Office of Naval Research
- United States Navy
- University of Iowa