Abstract:

A new recursive estimation algorithm, called the quasi-Gaussian Rao-Blackwellized particle filter (Q-GRBPF), is proposed for filtering mixed linear/nonlinear state space models. The algorithm utilizes the idea of RaoBlackwellized to separate the linear and nonlinear states. For the nonlinear states, the posterior distributions of the estimates, which are achieved by the quasi-Gaussian particle filter (Q-GPF), are approximated as Gaussian distributions. Also, the linear states are estimated by the Kalman filter(KF) with the estimated nonlinear states.

Key words: signal , processing,  , quasi-Gaussian , Rao-Blackwellized , particle , filter,  , simulation,  , mixed , linear/nonlinear,  , target , tracking