By Topic

The Algorithm of Track Occupied Identification Base on HMM

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wang Jian ; State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China ; ShangGuan Wei ; Cai Bo-gen ; Chen De-wang

Precise location of a train on the rail network is important to train control system. The general problem of locating a train on closely-spaced parallel tracks is hard to determine the track occupied by train simply relying on GNSS. Hidden Markov model (HMM) is widely used in speech processing of a time series model. This paper applied the HMM to the track occupied automatic identification, established the HMM of tracks, resolved the problem of track occupied identification using GNSS, and progressive studied the impact on identification, when changing the state number of the HMM, GNSS output frequency and train speed, then the optimal parameters are determined.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009