State Estimation of a Hybrid Markov Process with Application to Multitarget Tracking.

Abstract

Many estimation problems, including failure detection and multitarget tracking, rely on the state estimation of a hybrid Markov process. An algorithm for estimating the state sequence of such a process must keep the amount of computation at a reasonable level. Techniques are derived for reducing the computational burden, that do not increase the error probability. In a multiple system case, the concept of clustering, which greatly diminished the computational complexity, can be applied without loss of optimality under precise conditions. For the application to multitarget tracking, the computation of an optimal grating is done. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1982
Accession Number
ADA111728

Entities

People

  • Franck E. Bruneau

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Science
  • Damage Detection
  • Data Association
  • Detection
  • Detectors
  • False Alarms
  • Gaussian Distributions
  • Kalman Filtering
  • Markov Chains
  • Markov Processes
  • Military Research
  • Multitarget Tracking
  • Probability
  • Probability Distributions
  • Target Tracking

Readers

  • Distributed Systems and Data Platform Development
  • Sensor Fusion and Tracking Systems.
  • Statistical inference.