网络环境下非线性时变随机系统的最优递推滤波研究

项目名称： 网络环境下非线性时变随机系统的最优递推滤波研究

项目编号： No.11301118

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 数理科学和化学

项目作者： 胡军

作者单位： 哈尔滨理工大学

项目金额： 23万元

中文摘要： 本项目研究网络环境下的非线性时变随机系统的最优递推滤波器设计问题。首先归纳描述现有网络诱导现象的方法，建立刻画测量衰减、多重测量衰减、随机发生信号量化等新型现象的数学模型。综合考虑具有非线性性、时变性和随机性的物理对象及网络诱导现象，建立相应的非线性随机动力学模型与测量模型。接着，分别针对网络环境下的特殊/一般非线性时变随机系统构造依赖于网络诱导信息的递推滤波器。利用黎卡提型差分方程方法、新息分析方法、无损变换等技术，得到满足既定性能指标的递推滤波器的存在性条件及最优/次优递推滤波器的设计算法，给出保证滤波算法稳定性、鲁棒性与收敛性的判别条件，分别揭示滤波误差协方差对丢失概率的单调性、对衰减系数的鲁棒性和对量化极限的敏感性规律，并讨论计算复杂度和滤波性能之间的关系。本研究将有利于促进网络化系统、非线性随机系统和时变系统理论的发展，可为复杂动态网络化系统的信号估计提供有效的方法。

中文关键词： 非线性随机系统；时变系统；网络化诱导现象；递推滤波；故障估计

英文摘要： This project will discuss the design of the optimal recursive filters for the nonlinear time-varying stochastic systems in network environment. By summarizing the existing methods of characterizing the network-induced phenomena (NIP), the mathematical models will be given for the newly emerging NIP (including the fading measurements, multiple fading measurements and randomly occurring signal quantizations etc.). Considering the physical plant involving the features of nonlinear, time-varying as well as stochastic and the NIP, the nonlinear stochastic dynamic models and measurement models will be established. Subsequently, the recursive filters will be constructed in terms of the network-induced information for special/general nonlinear time-varying stochastic systems. By employing the techniques of Riccati-like difference equations, innovation analysis approach and unscented transformation, the existence of the recursive filtering with the specified performance indices will be given and the algorithms of the optimal/suboptimal recursive filters will be developed. Also, the critera will be proposed to ensure the stability, robustness and convergence of the developed filtering algorithms. The monotonicity of the missing probabilities, the robustness of the fading coefficients and the sensibility of the quantizati

英文关键词： nonlinear stochastic systems；time-varying systems；network-induced phenomena；recursive filtering；fault estimation