Monte Carlo filter particle filter
Masaya Murata, Hidehisa Nagano, Kunio Kashino · 2015
We propose a new realization method of the sequential importance sampling (SIS) algorithm to derive a new particle filter. The new filter constructs the importance distribution by the Monte Carlo filter (MCF) using sub-particles, therefore, its non-Gaussianity nature can be adequately considered while the other type of particle filter such as unscented Kalman filter particle filter (UKF-PF) assumes a Gaussianity on the importance distribution. Since the state estimation accuracy of the SIS algorithm theoretically improves as the estimated importance distribution becomes closer to the true posterior probability density function of state, the new filter is expected to outperform the existing, state-of-the-art particle filters. We call the new filter Monte Carlo filter particle filter (MCF-PF) and confirm its effectiveness through the numerical simulations.