Joint Parameter and State Estimation Based on Marginal Particle Filter and Particle Swarm Optimization

被引：7
作者：
Havangi, Ramazan [1 ]
机构：
[1] Univ Birjand, Fac Elect & Comp Engn, Birjand, Iran
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2018年 / 37卷 / 08期
关键词：
Extended marginal particle filter; Particle filter; PSO; Dual estimation; MODELS; IDENTIFICATION; ALGORITHMS; KALMAN;
D O I：
10.1007/s00034-017-0721-4
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a method for the dual estimation is proposed. This approach combines extended marginal particle filter (EMPF) with particle swarm optimization (PSO) for simultaneous estimation of state and parameter values in nonlinear stochastic state-space models. In the proposed method, the states are estimated by EMPF and the parameters are estimated by PSO. The performance of proposed algorithm is evaluated in two examples. Simulation results demonstrate the feasibility and efficiency of the proposed method.
引用
收藏
页码：3558 / 3575
页数：18
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