Application of sequential quasi-Monte Carlo to autonomous positioning

Nicolas Chopin, Mathieu Gerber

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

SMC (Sequential Monte Carlo) algorithms (also known as particle filters) are popular
methods to approximate filtering (and related) distributions of state-space
models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue
in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential
Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed
by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater
performance of SQMC on autonomous positioning problems.
Original languageEnglish
Title of host publicationSignal Processing Conference (EUSIPCO)
Subtitle of host publication 2015 23rd European
PublisherEuropean Association for Signal Processing (EURASIP)
ISBN (Electronic)978-0-9928-6263-3
Publication statusPublished - 2015

Keywords

  • Low-discrepancy point sets
  • Particle filtering
  • Quasi-Monte Carlo

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