Scalable Data and Sensor Fusion via Multiple-Agent Hybrid Systems

Abstract

We address the problem of finding an unbiased estimate of the plant state given that the data available is dynamic, noisy, and given in a multiplicity of representations. The approach proposed in the study is unique because it does not attempt to transform the data to a common representation. Rather we establish a framework, which we call the Multiple Agent Hybrid Estimation Architecture, in which we allow heterogeneous data to flow between individual agents in the network to improve their individual estimates of the current plant state.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 1998
Accession Number
ADA344362

Entities

People

  • A. Nerode
  • J. B. Remmel
  • W. Kohn

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algebra
  • Artificial Intelligence
  • Automata
  • Automata Theory
  • Coding
  • Communication Networks
  • Computational Science
  • Differential Equations
  • Differential Geometry
  • Equations
  • Hybrid Systems
  • Invariance
  • Lagrangian Functions
  • Power Series
  • Sensor Fusion
  • Side Effects
  • Topology

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation