Importance sampling for error event analysis of HMM frequency line trackers

M. Sanjeev Arulampalam, Rob J. Evans, Khaled Ben Letaief

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper considers the problem of designing efficient and systematic importance sampling (IS) schemes for the performance study of hidden Markov model (HMM) based trackers. Importance sampling (IS) is a powerful Monte Carlo (MC) variance reduction technique, which can require orders of magnitude fewer simulation trials than ordinary MC to obtain the same specified precision. In this paper, we present an IS technique applicable to error event analysis of HMM based trackers. Specifically, we use conditional IS to extend our work in another of our papers to estimate average error event probabilities. In addition, we derive upper bounds on these error probabilities, which are then used to verify the simulations. The power and accuracy of the proposed method is illustrated by application to an HMM frequency tracker.

Original languageEnglish
Pages (from-to)411-424
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume50
Issue number2
DOIs
Publication statusPublished - 1 Feb 2002

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Keywords

  • Error events
  • HMM
  • Importance sampling
  • Monte Carlo

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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