Accession Number:

ADA209411

Title:

Multisensor Modeling Underwater with Uncertain Information

Descriptive Note:

Doctoral thesis

Corporate Author:

WOODS HOLE OCEANOGRAPHIC INSTITUTION MA

Personal Author(s):

Report Date:

1988-09-01

Pagination or Media Count:

176.0

Abstract:

This thesis develops an approach to the construction of multidimensional stochastic models for intelligent systems exploring an underwater environment. The important characteristics shared by such applications are real-time constraints unstructured, three-dimensional terrain high-bandwidth sensors providing redundant, overlapping coverage lack of prior knowledge about the environment and inherent inaccuracy or ambiguity in sensing and interpretation. The models are cast as a three-dimensional spatial decomposition of stochastic, multisensor feature vectors that describe an underwater environment. Such models serve as intermediate descriptions that decouple low-level, high-bandwidth sensing from the higher-level, more asynchronous processes that extract information. A numerical approach to incorporating new sensor information--stochastic backprojection--is derived from an incremental adaptation of the summation method for image reconstruction. Error and ambiguity are accounted for by blurring a spatial projection of remote-sensor data before combining it stochastically with the model. By exploiting the redundancy in high-bandwidth sensing, model certainty and resolution are enhanced as more data accumulate. In the case of three- dimensional profiling, the model converges to a fuzzy surface distribution from which a deterministic surface map is extracted. Computer simulations demonstrate the properties of stochastic backprojection and stochastic models. Keywords Theses.

Subject Categories:

  • Physical and Dynamic Oceanography
  • Numerical Mathematics
  • Statistics and Probability
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE