Multisensor Modeling Underwater with Uncertain Information
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1988
- Accession Number
- ADA209411
Entities
People
- W. K. Stewart Jr.
Organizations
- Woods Hole Oceanographic Institution