Accession Number:

ADA214662

Title:

Least Squares Adaptive and Bayes Optimal Array Processors for the Active Sonar Problem

Descriptive Note:

Doctoral thesis

Corporate Author:

SCRIPPS INSTITUTION OF OCEANOGRAPHY LA JOLLA CA MARINE PHYSICAL LAB

Personal Author(s):

Report Date:

1989-10-01

Pagination or Media Count:

162.0

Abstract:

This dissertation examines the problem of detecting active sonar echoes under ocean reverberation limiting conditions. One detection approach is an ad-hoc engineering approach which preprocesses the received signal with an adaptive noise canceller, and then performs the detection. A second approach takes a global point of view and uses the known statistics of the problem to design an optimum detector in the Bayesian sense. A new, joint process, pole- zero adaptive filter is developed, and it is shown that under certain circumstances its performances is superior to its all-zero counterpart. It is a candidate for an adaptive, ad-hoc detection scheme. Often when closed expressions for system performance are hard to achieve, a Monte Carlo simulation approach is used. In this context, the ability to synthesize an ensemble of active sonar pings is a key to assessing detector performance. This dissertation develops a multichannel element level reverberation time series generator. Three active sonar problems of increasing complexity are examined. 1 the signal is known exactly with a boundary interference coming from a known direction 2 interference is coming from an uncertain direction 3 another fixed interference coming from an uncertain direction is added. For all the above problems, comparisons are made between the ad-hoc detector and two implementations of the Bayes detector, a block processor sub-optimum, and a time sequential processor. It is shown that when the optimum detector is allowed to be time sequential it performs uniformly better than the ad-hoc detector.

Subject Categories:

  • Acoustic Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE