Scalable Data and Sensor Fusion Through Optimal Solutions of Multiple Agent Systems
Abstract
This study formulated a distributed agent system for the implementation of an optimal fusion data strategy in a battlefield domain. The approach followed in the study is unique because it doesn't attempt to transform the data to a common representation, but rather, it uses a Multiple Agent Hybrid Estimation Architectural (MAHEA) framework. The optimal state estimate is obtained dynamically by the interation between the individual agents in the architecture over time. Each agent generate it's own state estimate. Each agent's estimate is improved over time by the heterogeneous data flowing from the other agents. The agents' basic block is a variation optimization algorithm and synchronization between agents is based on Noether's invariance principles. The architecture of the agents includes an adaptation mechanism for improving the estimates over time and also a planning element for generating the variational criterion online. The present study improves the formulation of the inference based procedure used for computing the optimal estimate used in an earlier version of the MAHEA architecture. The report illustrates the scheme and architecture with a simple example.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1998
- Accession Number
- ADA395383
Entities
People
- Jeffrey B. Remmel
- Wolf Kohn