Assessment of particle filter and Kalman filter for estimating velocity using odometery system

Purpose - The purpose of this paper is to propose multisensory integration for train positioning application, to support recent automatic train control systems and also moving block signaling systems.Design methodology approach - Reducing the cost and at the same time improving the reliability and a...

Full description

Saved in:
Bibliographic Details
Published in:Sensor review Vol. 30; no. 3; pp. 204 - 209
Main Authors: Khodadadi, A., Mirabadi, A., Moshiri, B.
Format: Journal Article
Language:English
Published: Bradford Emerald Group Publishing Limited 29.06.2010
Subjects:
Algorithms
Bias
Computer programming
Data analysis
Design engineering
Doppler effect
Doppler radar
Engineering
Estimating
Filtering
Measurement
Noise
Particle size measurement
Programming and algorithm theory
Railway engineering
Sensors
Signal processing
Studies
Tachometers
Trains
Velocity
Trains
Data analysis
Railway engineering
Particle size measurement
Velocity
Programming and algorithm theory
ISSN:0260-2288, 1758-6828
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose - The purpose of this paper is to propose multisensory integration for train positioning application, to support recent automatic train control systems and also moving block signaling systems.Design methodology approach - Reducing the cost and at the same time improving the reliability and accuracy of the overall positioning system, are primary goals of the researches going on in this field.Findings - This paper designs and evaluates two different algorithms of Kalman filtering (KF) and particle filtering (PF), on a set of low cost positioning systems, as tachometers, Doppler radar and balises.Originality value - This paper's research outcomes introduce considerable improvements upon the results when compared to the current utilization of the stand-alone tachometer and Doppler radar sensors, and slight improvements in comparison with the KF algorithm, and also upon results in recent publications on the subject.
Bibliography:original-pdf:0870300305.pdf
ark:/67375/4W2-34QNT1Q4-3
href:02602281011051380.pdf
filenameID:0870300305
istex:222FCD965B2951A93A06581785E98B3D09F37802
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0260-2288
1758-6828
DOI:10.1108/02602281011051380