A Study Of Hybrid Estimation Theory & Applications Based On Particle Filtering

With the development of science and technology, the scale and complexity of system is increasing and the demand for system performance is increasing. At the same time, the means of obtaining information is also increasing. As a result, the estimation for complex system under the complex and network environment becomes a front area of estimation theory. In this dissertation some key techniques for dynamic estimation of hybrid system are studied in details based on Monte Carlo particle filtering. The main contributions are as follows:1. An overview of the principle, convergence, development and application of Sequential Monte Carlo simulation based particle filtering is presented within Bayesian frameworks. The novel extensions and trends of particle filtering are also discussed.2. The iterative particle filter, fixed interval and fixed lag particle smoother are proposed for the state estimation of multiple model hybrid system. The proposed methods make use of structure characteristic of the model and combine the technique of Rao-Blackwellisation with Kalman technique. The simulation results show that the proposed filter and smoother are feasible and effective.3. The theoretical Cramer-Rao lower bound (CRLB) for hybrid estimation requires enumeration of all possible model sequences. The computational burden grows exponentially with time. An approximated formulation for the CRLB is proposed which makes use of a subset of model sequence hypotheses. The approximated computation of the CRLB is achieved through particle filtering and Monte Carlo simulation. The simulation shows the effectiveness and feasibility of the proposed method.4. An adaptive Monte Carlo estimation algorithm based on particle filtering is proposed for hybrid system with unknown transition probabilities. A set of random samples from state space are utilized to explore the evolution of state and models. The transition probabilities, state and model probabilities are estimated online simultaneously. An adaptive estimation algorithm for unknown parameter in dynamic...