A Hybrid Importance Function for Particle Filtering

Particle filtering has drawn much attention in recent years due to its capacity to handle nonlinear and non-Gaussian dynamic problems. One crucial issue in particle filtering is the selection of the importance function that generates the particles. In this letter, we propose a new type of importance function that possesses the advantages of the posterior and the prior importance functions. We demonstrate its use on the problem of blind detection in flat fading channels and provide simulation results that show its efficiency and performance.