A Framework for Mixed Estimation of Hidden Markov Models

Abstract

In this paper, we present a framework for a mixed estimation scheme for hidden Markov models (HMM). A robust estimation scheme is first presented using the minimax method that minimizes a worst case cost for HMMs with bounded uncertainties. Then we present a mixed estimation scheme that minimizes a risk-neutral cost with a constraint on the worst-case cost. Some simulation results are also presented to compare these different estimation schemes in cases of uncertainties in the noise model.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA438575

Entities

People

  • Steven I Marcus
  • Subhrakanti Dey

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Communication Systems
  • Computational Science
  • Dynamic Programming
  • Electrical Engineering
  • Engineering
  • Hidden Markov Models
  • Markov Chains
  • Markov Models
  • Measurement
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.