Improved Particle Filters for Vehicle Localisation

The ability to track a moving vehicle is of crucial importance in numerous applications. The task has been often approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. The performance of the particle filters method is strongly dependent on the choice of the proposal distribution. In this paper, we introduce a proposal distribution that samples around the most recent sensor observation. The proposal leads to an order of magnitude improvement in accuracy and efficiency over conventional particle filters, especially when the sensor noise is low.