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
Robust Constrained Variance Estimation for Discrete Systems with Model Noise Intensity Uncertainty and its Application
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摘要: 考虑离散随机系统在模型噪声强度不确定及估计误差方差受约束情形下的一类鲁棒状态 估计问题,即希望找到这样的滤波增益,使得当模型噪声强度在一定范围内变动时,每个状态 分量的估计误差方差始终不大于预先指定值.文中给出了这种滤波增益的设计方法,并以一 类机动目标跟踪问题为例,说明这种设计方法的直接性与有效性.Abstract: 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.
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Key words:
- Discrete stochastic systems /
- state estimation /
- robust estimation
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