Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric Wan
In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented fil(cid:173) ters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms stan(cid:173) dard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.