A sequential Monte Carlo approximation of the HISP filter

Jeremie Houssineau, Daniel E Clark, Pierre Del Moral

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

A formulation of the hypothesised filter for independent stochastic populations (hisp) is proposed, based on the concept of association measure, which is a measure on the set of observation histories. Using this formulation, a particle approximation is introduced at the level of the association measure for handling the exponential growth in the number of underlying hypotheses. This approximation is combined with a sequential Monte Carlo implementation for the underlying single-object distributions to form a mixed particle association model. Finally, the performance of this approach is compared against a Kalman filter implementation on simulated data based on a finite-resolution sensor.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages1251-1255
Number of pages5
ISBN (Print)9780992862633
DOIs
Publication statusPublished - 2015
Event23rd European Signal Processing Conference 2015 - Nice, France
Duration: 31 Aug 20154 Sep 2015

Publication series

NameProceedings of the European Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Print)2076-1465

Conference

Conference23rd European Signal Processing Conference 2015
Abbreviated titleEUSIPCO 2015
CountryFrance
CityNice
Period31/08/154/09/15

Keywords

  • finite-resolution sensor
  • Multi-object filtering

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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