Constrained Batch Estimation for Train Positioning with Inertial Sensors

Similar to current efforts in the automotive industry, there is a substantial interest in developing fully autonomous trains. One of the key steps towards enabling autonomous operation is being able to accurately estimate train position and speed. In addition to this, better estimates will also increase safety and reduce distance between trains, allowing more frequent trains in peak hours. The current project deals with a method of estimating the velocity and position without using GPS measurements, which is the standard method. This is because GPS signal can be unreliable in urban environments and are unavailable underground in subway networks. Instead of using measurements one by one, the position and velocity are estimated by using batches of measurements and fitting them to the track, which the train always travels on. For Thales, this presents an alternative method of estimating velocity and position that does not require multiple sensors, adding reliability and thus safety to existing solutions.

Faculty Supervisor:

James Richard Forbes

Student:

Marc-Antoine Lavoie

Partner:

Thales Canada Inc.

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

McGill University