Introduction to Box Particle Filtering

Resulting from the synergy between the sequential Monte Carlo (SMC) method [1] and interval analysis [2], box particle filtering is a recently emerged approach aimed at solving a general class of nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements that result in very broad posterior densities. It relies on the concept of box particle that occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sequential importance resampling (SIR) PF may require thousands of particles to achieve accurate and reliable performance, the Box-PF can reach the same level of accuracy with just a few dozens of box particles. This lecture note presents a quick introduction to this new methodology, emphasizes the need for interval tools, and gives a Bayesian interpretation of the Box-PF. Some familiarity with particle filters is assumed.