Title: Filter-based recursive Bayesian algorithm with modified covariance resetting for non-uniformly sampled data systems
Authors: Shaoxue Jing; Tianhong Pan; Zhengming Li
Addresses: School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; Department of Electrical Engineering, Huaian College of Information and Technology, Huaian, Jiangsu 223003, China ' School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Electrical Information and Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract: To identify a system with non-uniformly sampled data, a recursive Bayesian algorithm combined dynamic filter with covariance resetting is proposed. First, the input-output data is filtered by the estimated noise transfer function, and the system is decomposed into two fictitious sub-systems with a low dimension. Second, the estimated variance of the noise is employed in the proposed algorithm to improve the estimates. Furthermore, an efficient covariance resetting strategy is integrated into the algorithm to increase the convergence rate. Finally, the proposed algorithm is validated by a numeric example.
Keywords: filter; recursive Bayesian algorithm; covariance resetting; non-uniformly sampled data systems.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.3, pp.173 - 180
Available online: 22 Apr 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article