How to tell a bad filter through Monte Carlo simulations

Lingji Chen, Chihoon Lee, Raman K. Mehra

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this note, we propose one particular method to address the issue of how to numerically evaluate nonlinear filtering algorithms and/or their software implementations, through Monte Carlo simulations. We introduce a quantitative performance indicator whose computation can be automated and does not depend on any specific definition of point estimate. The method is based on conditional probability integral transform and maximum deviation of an empirical cumulative distribution function from a uniform distribution. The usefulness of such an indicator is illustrated through an example.

Original languageEnglish
Pages (from-to)1302-1307
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume52
Issue number7
DOIs
StatePublished - Jul 2007

Keywords

  • Algorithm
  • Conditional cumulative density function
  • Density evaluation
  • Implementation
  • Kolmogorov-Smirnov goodness-of-fit test
  • Monte Carlo simulations
  • Nonlinear filtering
  • Performance
  • Probability integral transform

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