  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.
