Parameter Estimation In State Space Model Based On Segmentational Particle Filtering

Kalman filter can estimate optimal parameter in linear Gaussian state space model,but it can't be applied in the nonlinear/non-Gaussian state space model.In view of the complexity of the filtering and the increasing accuracy requirements,the traditional nonlinear filtering method has been difficult to meet the actual application requirements.As one of the new nonlinear filtering method,particle filtering is not limited by the system and noise distribution,which is more in line with the requirements of the actual filtering task.Therefore,it is widely concerned in the application of parameter estimation of nonlinear and non-Gaussian state space model.However,there are still some problems to be solved in the process of rapid development of particle filtering,especially the problem of particle degradation,which has influenced the development and application of particle filtering,and has a large deviation on the parameter estimation of nonlinear and non-Gaussian state space model.Therefore,the improvement of particle filtering method is of great significance to the parameter estimation in nonlinear and non-Gaussian state space model,perfecting filtering theory and expanding its application field.In order to solve the problem of parameter estimation performance degradation due to particle degradation,segmentational particle filtering based on sequential importance resampling particle filtering algorithm is present.Segmentational particle filtering divides the observation data into segments and estimates the parameters of each segment based on sequential importance resampling particle filtering.Finally,the parameter estimation of each segment are combined by using meta-analysis.Experimental results show that segmentational particle filtering effectively alleviates the particle degradation problem,having better estimation efficiency with roughly the same calculation and the characteristic of parallel calculation.And the improved algorithm has the characteristics of on-line estimation,overcoming the drawback of Markov Chain Monte Carlo.Also,segmentational particle filtering further improves the particle filtering theory and expands its application field.