A biological inspired improvement strategy for particle filters

J. P. Zhong; Y. F. Fung

doi:10.1109/ICIT.2009.4939539

A biological inspired improvement strategy for particle filters

J. P. Zhong, Y. F. Fung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Particle Filters (PF) is a model estimation technique based on simulation. But two problems, namely particle impoverishment and sample size dependency, frequently occur during the particle updating stage and these problems will reduce the accuracy of the estimation results. In order to avoid these problems, Ant Colony Optimization is incorporated into the generic particle filter before the updating stage. After the optimization, particle samples will move closer to their local highest posterior density function and better estimation results can be produced.

Original language	English
Title of host publication	2009 IEEE International Conference on Industrial Technology, ICIT 2009
DOIs	https://doi.org/10.1109/ICIT.2009.4939539
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Conference on Industrial Technology, ICIT 2009 - Churchill, VIC, Australia Duration: 2009 Feb 10 → 2009 Feb 13

Other

Other	2009 IEEE International Conference on Industrial Technology, ICIT 2009
Country/Territory	Australia
City	Churchill, VIC
Period	09/2/10 → 09/2/13

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications

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10.1109/ICIT.2009.4939539

Cite this

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title = "A biological inspired improvement strategy for particle filters",

abstract = "Particle Filters (PF) is a model estimation technique based on simulation. But two problems, namely particle impoverishment and sample size dependency, frequently occur during the particle updating stage and these problems will reduce the accuracy of the estimation results. In order to avoid these problems, Ant Colony Optimization is incorporated into the generic particle filter before the updating stage. After the optimization, particle samples will move closer to their local highest posterior density function and better estimation results can be produced.",

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doi = "10.1109/ICIT.2009.4939539",

language = "English",

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note = "2009 IEEE International Conference on Industrial Technology, ICIT 2009 ; Conference date: 10-02-2009 Through 13-02-2009",

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AU - Zhong, J. P.

AU - Fung, Y. F.

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AB - Particle Filters (PF) is a model estimation technique based on simulation. But two problems, namely particle impoverishment and sample size dependency, frequently occur during the particle updating stage and these problems will reduce the accuracy of the estimation results. In order to avoid these problems, Ant Colony Optimization is incorporated into the generic particle filter before the updating stage. After the optimization, particle samples will move closer to their local highest posterior density function and better estimation results can be produced.

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U2 - 10.1109/ICIT.2009.4939539

DO - 10.1109/ICIT.2009.4939539

M3 - Conference contribution

AN - SCOPUS:67650340139

SN - 1424435064

SN - 9781424435067

BT - 2009 IEEE International Conference on Industrial Technology, ICIT 2009

T2 - 2009 IEEE International Conference on Industrial Technology, ICIT 2009

Y2 - 10 February 2009 through 13 February 2009

ER -

A biological inspired improvement strategy for particle filters

Abstract

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