Robust Constrained Variance Estimation for Discrete Systems with Model Noise Intensity Uncertainty and its Application
离散系统的鲁棒约束方差估计及应用——模型噪声强度不确定情形

In this paper, the problem of robust state estimation for linear stochastic systems with model noise intensity uncertainty and state estimation error variance constraints is considered. The goal of this problem is to find the filter gain such that the estimation error variance of each state is less than or equal to the prescribed value, when the model noise intensity varies in a certain range. The design method for such a filter gain is given in the present paper. An example dealing with the problem of tracking maneuvering targets, is provided to demonstrate the directness and effectiveness of this method.