Acoustic Color Quality Assessment (ACQUA)

Abstract

There is growing interest in using acoustic color, which is the aspect-dependent target strength response of an object, as a data representation suitable for feature extraction to improve ATR (automatic target recognition) performance for mine countermeasures (MCM). Compared to SAS (synthetic aperture sonar) imagery, acoustic color is believed to be less sensitive to certain uncompensated errors for the purpose of detecting and classifying underwater mines. The goal of this project is to establish the means by which degradations in acoustic color performance can be characterized and understood. Where possible, this understanding will be expressed in the context of an ATR feature space. We have investigated various signal processing methods for acoustic color and methods for quantifying the quality of acoustic color. The work accomplished during this effort can be summarized as follows: 1) Investigated the representation space for acoustic color and other related spatial spectrum domains to demonstrate the utility of transforming between these domains for the purpose of assessing information quality, 2) Development of two different definitions of acoustic color quality primarily utilizing the phase of acoustic color, 3) Investigation of representation domain trade-offs with respect to the acoustic color quality, 4) Demonstration of the utility of developed quality measures.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2019
Accession Number
AD1083867

Entities

People

  • J. Daniel Park

Organizations

  • Pennsylvania State University

Tags

DTIC Thesaurus Topics

  • Classification
  • Contract Administration
  • Contracts
  • Feature Extraction
  • Information Operations
  • Military Research
  • Organizational Structure
  • Recognition
  • Signal Processing
  • Standards
  • Synthetic Aperture Sonar
  • Target Recognition
  • Target Strength
  • Underwater Mines

Readers

  • Acoustical Oceanography.
  • Instructional Design and Training Evaluation.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • Space