Particle Filtering With Adaptive Number of Particles
Nonlinear/non-Gaussian dynamic systems can be tackled by a number of filtering methods. The authors are interested in particle filters, which perform a discrete characterization of the posterior distribution of the system based on a random set of points. The dimension of the random set is a design issue and typically large values are required to ensure proper tracking of the system. This is typically solved by a worst case criterion, involving a waste of computational resources. In this paper, they are interested in the design of a particle filtering algorithm which is able to adapt the dimension of its particle pool.