Novel adaptive particle filter using adjusted variance and its application

Sang Hyuk Park; Young Joong Kim; Myo Taeg Lim

doi:10.1007/s12555-010-0412-4

Novel adaptive particle filter using adjusted variance and its application

Sang Hyuk Park, Young Joong Kim, Myo Taeg Lim

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.

Original language	English
Pages (from-to)	801-807
Number of pages	7
Journal	International Journal of Control, Automation and Systems
Volume	8
Issue number	4
DOIs	https://doi.org/10.1007/s12555-010-0412-4
Publication status	Published - 2010 Aug

Bibliographical note

Funding Information:
Manuscript received July 11, 2008; revised August 12, 2009; accepted February 2, 2010. Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Jae-Bok Song. The authors would like to acknowledge the Ministry of Land, Transport and Maritime Affairs, Korea. This work is supported by the 2006 High-Tech Fusion Construction Technology Development Program [06 High-Tech Fusion D01].

Keywords

Kullback-leibler distance
mobile robot
particle filter
ultrasonic beacon

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications

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10.1007/s12555-010-0412-4

Cite this

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title = "Novel adaptive particle filter using adjusted variance and its application",

abstract = "Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.",

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N2 - Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.

AB - Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.

KW - Kullback-leibler distance

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Novel adaptive particle filter using adjusted variance and its application

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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