摘要
The globally optimal recursive filtering problem is studied for a class of systems with random parameter matrices,stochastic nonlinearities, correlated noises and missing measurements. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the additive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as well as two-step cross-correlated.A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by unfavorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is globally minimized at each sampling time. A numerical simulation example is provided to illustrate the effectiveness and applicability of the proposed algorithm.
The globally optimal recursive filtering problem is stu- died for a class of systems with random parameter matrices, stochastic nonlinearities, correlated noises and missing measure- ments. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the addi- tive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as well as two-step cross-correlated. A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by un- favorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is globally minimized at each sampling time. A numerical simulation example is provided to illustrate the effectiveness and applicability of the proposed algorithm.
基金
supported by the National Natural Science Foundation of China(61233005)
the National Basic Research Program of China(973 Program)(2014CB744200)
关键词
随机非线性系统
不确定系统
最优滤波
相关噪声
缺失值
测量方法
递归滤波器
参数矩阵
globally optimal recursive filtering, random parame- ter matrices, stochastic nonlinearities, correlated noises, missing measurements