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This paper presents a new method to draw particles for the particle filter in the case of large state noise. 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...
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...
We present a new unscented particle filter for dynamic systems that outperforms the general particle filter and the unscented particle filter when the variance of the observation noise is small. Our algorithm uses a bank of unscented Kalman filters to refine the prediction in particle filter. The key difference with the traditional unscented particle filter is the introduction of an auxiliary model...
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