Convergence of self-tuning Riccati equation with correlated noises |
| |
Authors: | Guili TAO and Zili DENG |
| |
Affiliation: | 1. Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China;Computer and Information Engineering College, Heilongjiang Institute of Science and Technology, Harbin Heilongjiang 150027, China 2. Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China |
| |
Abstract: | For the linear discrete time-invariant stochastic system with correlated noises, and with unknown model parameters and noise
statistics, substituting the online consistent estimators of the model parameters and noise statistics into the optimal time-varying
Riccati equation, a self-tuning Riccati equation is presented. By the dynamic variance error system analysis (DVESA) method,
it is rigorously proved that the self-tuning Riccati equation converges to the optimal time-varying Riccati equation. Based
on this, by the dynamic error system analysis (DESA) method, it is proved that the corresponding self-tuning Kalman filter
converges to the optimal time-varying Kalman filter in a realization, so that it has asymptotic optimality. As an application
to adaptive signal processing, a self-tuning Kalman signal filter with the self-tuning Riccati equation is presented. A simulation
example shows the effectiveness. |
| |
Keywords: | Kalman filter Self-tuning filter Riccati equation Lyapunov equation Convergence |
本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录! |
| 点击此处可从《控制理论与应用(英文版)》浏览原始摘要信息 |
|
点击此处可从《控制理论与应用(英文版)》下载全文 |
|