Accession Number:

ADA218133

Title:

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

Descriptive Note:

Technical rept.

Corporate Author:

NAVAL UNDERWATER SYSTEMS CENTER NEWPORT RI

Personal Author(s):

Report Date:

1989-05-15

Pagination or Media Count:

95.0

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.

Subject Categories:

  • Acoustic Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE