Constrained Kalman filtering for train position estimation

Just like for the automotive industry, there is growing interest in the development of fully autonomous trains. One of the key steps in the creation of a fully autonomous solution is optaining an accurate estimate of the train position and velocity. Accurate estimates are critical component of the train safety during operation and better estimates allow more trains to operate safely on the same track. The current project deals with trains operating in areas without GPS coverage, such as subways, and so accurate position measurements cannot be obtained as frequently. This means that the estimation algorithm used to calculate the position in between measurements must be as precise as possible to reduce any possible drift. The proposed method to improve the estimate is to ensure that the train states (position, velocity, train orientation) are explicitly constrained by the track the train is traveling on. TO BE CONT’D

Faculty Supervisor:

James Forbes

Student:

Marc-Antoine Lavoie

Partner:

Thales Canada Inc.

Discipline:

Engineering - mechanical

Sector:

Automotive and transportation

University:

Program: