Bayesian filters provide a robust and powerful technique for integrating noisy information in dynamic environments. However, the computational cost of the filtering algorithm depends on the size of the problem, and an effective solution may be constrained by execution time. This paper applies basic concepts of clustering and message passing to particle filters making them substantially faster to compute, while still maintaining the original accuracy. An example from vehicle state estimation is provided to illustrate how to implement the technique. Our results indicate that modularization can produce a speed up of over 28 times even on this small problem.
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