We describe a new particle filtering algorithm, called the augmented particle
filter (APF), for online filtering problems in state space models. The APF
combines information from both the observation equation and the state
equation, and the state space is augmented to facilitate the weight
computation. Theoretical justification of the APF is provided, and the
connection between the APF and the optimal particle filter in some special
state
space models is investigated. We apply the APF to several examples to
demonstrate the effectiveness of the method.