Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14455
Title: A biologically inspired improvement strategy for particle filter : ant colony optimization assisted particle filter
Authors: Zhong, J
Fung, YF
Dai, M
Keywords: Ant colony optimization
Filtering theory
Model estimation
Particle filters
Issue Date: 2010
Publisher: Inst Control Robotics & Systems, Korean Inst Electrical Engineers
Source: International journal of control, automation and systems, 2010, v. 8, no. 3, p. 519-526 How to cite?
Journal: International Journal of Control, Automation and Systems 
Abstract: Particle Filter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony Optimization is incorporated into the generic PF before the updating stage. After executing the Ant Colony optimization, impoverished particle samples will be re-positioned and closer to their locally highest likelihood distribution function. Our experimental results show that the proposed algorithm can realize better tracking performance when comparing to the generic PF, the Extended Kalman Filter and other enhanced versions of PF.
URI: http://hdl.handle.net/10397/14455
DOI: 10.1007/s12555-010-0304-7
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

12
Last Week
0
Last month
0
Citations as of Nov 23, 2017

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
0
Citations as of Sep 29, 2017

Page view(s)

63
Last Week
0
Last month
Checked on Nov 19, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.