Research on Unknown Static Parameter Estimation Problem of State Space Models Based on Particle Filter Algorithm

Shang Bin Jiao; Fu Cai Qian; Jun Yang

doi:10.4028/www.scientific.net/AMR.532-533.1820

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Research on Unknown Static Parameter Estimation...

Research on Unknown Static Parameter Estimation Problem of State Space Models Based on Particle Filter Algorithm

Abstract:

A novel parameter estimation method for unknown static parameters of the state space model using particle filtering (PF) has proposed in this paper. Traditional methods enlarge state vector by treating the unknown parameter θ as a part of state vector (x_k,θ) . But this may cause the degeneration of θ, when some estimates become too small to continue as a result of the non-dynamic character of parameters if θ at time k is only determined by time k-1. Compared to traditional methods, this novel method assumes that the posterior distribution of θ is given by previous observation and state vectors, z_1:k and x_1:k. Obtain statistics at time k by using the integration of z_1:k and x_1:k, and solve parameter estimation problem by updating θ recursively. Good results are obtained when this method is used in different models.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1820-1824

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1820

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Online since:

June 2012

Authors:

Shang Bin Jiao, Fu Cai Qian, Jun Yang

Keywords:

Parameters Estimation, Particle Filter (PF), Sequential Updating, State Space Model

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