Hybrid Estimation Of The Adaptive Smoothing Algorithm

This thesis is focused on several fundamental problems of hybrid estimation techniques, which becomes a most important field of adaptive filtering in the recent two decades. The main contributions are as follows:1 . Interacting Multiple Model Algorithm (IMM) has been demonstrated to be the most cost-effective algorithm in hybrid estimation. The transient process of IMM is analyzed via Monte Carlo simulation. Simulation results shows that the residual of each filter in IMM is still Gaussian distribution, but its mean is not zero if the dominating filter cannot match the real target model.2. After reviewing several smoothing algorithms for hybrid estimation ,we presented a sub optimal approach to the d step fixed-lag smoothing problem for Markovian switching system by applying the basic IMM structure to the system with augmented system state and mode probability. The new fixed-lag smoothing. algorithm can provide us with the smoothed model probability that can be used to judge the system jump or abruptly change.3. The smoothing algorithms are compared in two typical target tracking simulation examples. Simulation results show that a significant improvement on the filtering algorithm is achieved by smoothing algorithms at the cost of a small time delay. The new smoothing algorithm proposed in this thesis is superior to other smoothing algorithms.4. A new enhanced IMM algorithm based on IMM smoothing algorithm is presented. Simulation results show that this new algorithm is superior to standard IMM algorithm without time delay.5. The mechanism of Unscented Kalman filter (UKF) is analyzed and a new multiple model estimator based on UKF and IMM is proposed for maneuver target tracking with non-linear measurements. Simulation results show the superiority of this new algorithm in dealing with such systems.6. We discuss a new popular approach, which is called particle filter, to state estimation problem for non-linear, non-Gaussian system. In addition, some potential problems of the particle filter have been presented.