Particle Filter with LMMSE Importance Sampling

Nonlinear estimation can be performed in many ways, with the particle filter being one of the most common. It is well known that ignoring the latest measurement value in the choice of importance density can result in poor particle filter performance for certain classes of challenging problems. In this paper a novel particle filter with importance density based on the linear minimum mean square error (LMMSE) estimator is presented. Performance is evaluated using Monte Carlo simulations of a highly nonlinear growth model. The proposed algorithm is compared with existing particle filter formulations using metrics for accuracy, consistency, and particle diversity.