NDLI: Train distance and speed estimation using multi sensor data fusion

Content Provider	IET Digital Library
Author	Muniandi, Ganesan Deenadayalan, Ezhilarasi
Abstract	The accurate distance and speed estimates of train and individual coaches are necessary for the safe operation of the high-speed train system. Often, the train system does not rely on a single sensor for its distance and speed measurements as the sensors are susceptible to diverse operating conditions such as snow, rain, fog, tunnel, hilly region, slip, slide, etc. Hence, the information from a combination of sensors which can complement each other under certain operating conditions is required for the correct estimation. For distance measurements, Global Navigation Satellite System (GNSS) and balise are generally used. For speed sensing, the combination of wheel sensor, radar and GNSS are chosen. The diversity of sensors in terms of sampling rate and noise characteristics, etc. greatly affect the overall estimation accuracy and reliability if the measurements are used directly. Hence, this work presents a probabilistic weighted fusion algorithm which is based on the nonlinear longitudinal train dynamic model. The fusion algorithm combines the state estimates from distributed and sensor-specific extended Kalman filters. The effectiveness of the proposed fusion algorithm is demonstrated on the simulated sensor measurements along with a wide range of noises, spurious measurements, train operating conditions and track environmental disturbances.
Starting Page	664
Ending Page	671
Page Count	8
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 4, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5359
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-11-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acceleration Continuous Spot Train Distance Measurement Distance Measurement Global Navigation Satellite System GNSS High-speed Train System Kalman Filter Multisensor Data Fusion Noise Characteristic Nonlinear Filter Probabilistic Weighted Fusion Algorithm Probability Probability Theory Radio Navigation And Direction Finding Reliability Rotation Measurement Satellite Navigation Sensor Fusion Sensor-specific Distributed Extended Kalman Filter Spatial Variables Measurement Speed Estimation Algorithm Speed Measurement Speed Sensing State Estimation Statistics Stochastic Linearised SCUC Train Distance Estimation Tunnel Velocity Velocity Measurement Wheel Sensor
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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