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The Constrained Extended Kalman Particle Filter

Particle filtering (PF) schemes are a set of simulation-based techniques relying on proposal distributions which have a crucial impact on their performance. In this paper, we introduce a novel constrained Extended Kalman particle filter (CEPF), the constrained Extended Kalman filter is used to generate the porposal distribution. The algorithm integrates the nonlinear state constraint information and the latest measurement information simultaneously into the dynamic system transition density. The proposed algorithm selects particles with higher likelihood to propagate into the next time ste by an efficient series of constraint optimization. The method can convergent theoretically and the simulation results show estimate advantage compared with other convention filters such as Extended Kalman Filter (EKF), Unscented Kalman Filer (UKF), Generic Particle Filter(GPF), particle filter with EKF proposal (EPF) and particle filter with UKF proposal (UPF).