Integrated Decision, Estimation and Communication Theories
Abstract
The objectives of this Program were to investigate the synergies among the decision, estimation and communication aspects of a distributed multisensor system. Hence. the effort in this project was primarily concentrated in the development of a coherent framework that would allow the development of a coherent theory of distributed decision and incorporate estimation and communication aspects. In this context, a NeymanPearson theory for distributed decision fusion was developed. The effect of communications and topological aspects in the structure and performance of the optimal distributed decision fusion have been investigated. The optimal distributed Neyman-Pearson decision fusion has been derived for the ideal case, and in cases where transmission delays, channel errors, and sensor misalignment are present. Other issues involved in the design of a distributed decision fusion system, such as intersensor correlation and multiresolution detection have also been investigated. A Generalized Evidence Processing theory that extends and to certain extend unifies. the Bayesian and Dempster-Shafer theories has been developed. A systematic framework for the data fusion analysis and synthesis has also been developed and tested with experimental data successfully. Distributed, Decision, Fusion, Estimation, Communication.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 26, 1993
- Accession Number
- ADA277084
Entities
People
- Stelios C. Thomopoulos
Organizations
- Pennsylvania State University