A Bayesian Approach to Acoustic Imaging and Object Classification by High Frequency Sonar

Abstract

The active sonar classification problem is approached as a likelihood ratio test of multiple, alternative hypotheses versus a noise-only null hypothesis. The data are, in general, vector-valued stochastic processes representing measurements from individual elements within a sonar array. An explicit form is assumed for the received signal model, which is statistically characterized for each alternative hypothesis (target class). Explicit results are derived for the likelihood ratio, and various performance characteristics are shown. Moreover, the optimal processor is examined from the perspective of acoustic image processing. Generalizations of the results are indicated and in some cases addressed in detail (e.g., the case of moving targets).

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Document Details

Document Type
Technical Report
Publication Date
May 15, 1989
Accession Number
ADA218133

Entities

People

  • J. G. Kelly
  • R. N. Carpenter

Organizations

  • Naval Underwater Systems Center

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Coordinate Systems
  • Detection
  • Detectors
  • Distribution Functions
  • Eigenvalues
  • Equations
  • Geometry
  • Integral Equations
  • Linear Arrays
  • Machine Learning
  • Probability
  • Probability Density Functions
  • Random Variables
  • Stochastic Processes
  • Unmanned Underwater Vehicles

Readers

  • Mathematical Modeling and Probability Theory.
  • Radar Systems Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference