Improved Particle Filters for Vehicle Localisation
This work addresses vehicle localization for applications like tracking, but it is incremental as it builds on existing particle filter techniques.
The paper tackled the problem of particle filters performing poorly with highly informative observations by proposing filters that sample around the most recent observation, resulting in an order of magnitude improvement in accuracy and efficiency over conventional methods.
The ability to track a moving vehicle is of crucial importance in numerous applications. The task has often been 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. Particle filters perform poorly when observations are highly informative. In this paper, we address this problem by proposing particle filters that sample around the most recent observation. The proposal leads to an order of magnitude improvement in accuracy and efficiency over conventional particle filters, especially when observations are infrequent but low-noise.