Interacting multiple model particle filtering algorithm based on generalized unscented transformation

To solve the particles impoverishment and the real-time decline caused by re-sampling and the proposal distribution optimization, a novel particle filtering algorithm based on generalized unscented transformation is proposed. The generalized unscented transform and the one-step state prediction and observation update of Kalman filter are introduced to realize the optimization of state estimation by the latest observation information. Then, by means of combining the improved algorithm with interacting multiple model, the interacting multiple model particle filtering algorithm based on generalized unscented transformation is proposed. The theoretical analysis and experimental results show that the new algorithm is close to the standard particle filter in computational complexity and superior to the standard particle filter and its improved algorithms in precision.