随机模态驱动下动态过程贝叶斯递推估计

项目名称： 随机模态驱动下动态过程贝叶斯递推估计

项目编号： No.61273087

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 刘飞

作者单位： 江南大学

项目金额： 81万元

中文摘要： 信号滤波或估计是控制理论与工程中非常活跃的研究领域，从模拟滤波器、数字滤波器，进而统计滤波器，至Kalman滤波器实现了线性高斯系统的最优状态估计，成为现代控制理论的里程碑。从统计估计的角度，贝叶斯估计独特的不确定性表达能力以及符合工程应用的递推结构，可进一步超越Kalman滤波的限制；另一方面，目前状态估计主要针对基于时间演化的动态系统，而现代工程领域中，存在大量既含时间状态演化，又有事件模态驱动的所谓混杂动态系统，需要新的可以处理模态驱动机制的状态估计理论和方法。本项目研究随机模态驱动下动态过程的贝叶斯估计问题，以随机模态驱动机制分析及观测信息的统计利用为主线，依据贝叶斯法则，吸取交互多模型算法的主要思想，提出模态和状态混杂过程的估计理论；进而针对不确定性、非线性、时滞等复杂动态，围绕递推估计方法的鲁棒性、精度和计算耗时等展开研究。项目具有重要而深刻的理论意义，也有广泛的工程应用价值。

中文关键词： 随机跳变系统；多模型；贝叶斯估计；鲁棒性；滤波

英文摘要： Control theory and engineering have focused on signal filtering or estimation for long. From the analog filters, digital filters to statistical filters, Kalman filter provides the optimal estimation for linear Gaussian systems and is considered a milestone in modern control theory. From the view of statistical estimation, Bayesian estimation can overcome the limitations of Kalman filter due to its distinct advantages in representing uncertainties and the recursive structure preferred by engineering applications. On the other hand, the existing state estimation methods almost concentrate on the time-driven systems, however, the systems involved both time-evoloving and event-driven mechanisms are encountered commonly in applications. Therefore, it is highly desired to propose some novel estimation theory which can handle mode-driven mechanisms effectively. This project explores the Bayesian estimation of dynamic process driven by stochastic modes based on the analysis of the mechanisms of random mode and the statistics of observational information. By using the main idea of the so-called interacting multiple-model techniques together with Bayesian principle,the estimation theory is developed for the hybrid process with both mode and state. Moreover aim at the complicated dynamics, such as uncertainties, non-linear

英文关键词： Stochastic jump system；multiple model；Bayesian estimation；robustness；filtering