Train localization using an adaptive multisensor data fusion technique

This work deals with the development of an adaptive multisensor data fusion technique for the accurate estimation of the trains position and velocity. The proposed technique will work with the Train Collision Avoidance System (TCAS) used in Indian railways during Global Positioning System (GPS) outages. The determination of accurate position of trains is a challenging task for the TCAS during GPS outages. The accuracy of the proposed Volterra Recursive Least Square (VRLS) based adaptive multisensor data fusion technique is evaluated by generating two kinematic profiles for a passenger train running between Silchar–Lumding broad gauge route in Indian railways. The effect of accelerometer bias is also considered during the analysis. It is observed that the developed technique can provide a better estimate of the position and velocity for the TCAS especially during GPS outages and without using any additional railway infrastructure. The simulation results indicate that the proposed technique is superior to the earlier works in terms of achieving better positional accuracy in presence of accelerometer bias