A modified bootstrap filter

This paper presents a new method to draw particles in the particle filter. The standard bootstrap filter draw particles randomly from the prior density which does not use the latest information of the observation. Some improvements consist in using extended Kalman filter or unscented Kalman filter to produce the importance distribution in order to move the particles from the domain of low likelihood to the domain of high likelihood by using the latest information of the observation. These methods work well when the state noise is small. We propose a modified bootstrap filter which uses a new method to draw the particles in the scenario of a big state noise. We show through numerical examples that it outperforms the bootstrap filter with the same computational complexity.