A New Robust Minimum Variance Filter for Uncertain Discrete-Time Systems
-
摘要: 提出了一种线性离散不确定系统鲁棒最小方差滤波新算法.当离散系统含有不确定 项时,能保证滤波误差的方差有界且最小.同已有算法相比,该算法只需要求解两个相互独立 的离散Riccati方程,可大大降低鲁棒滤波的计算和分析难度.通过对算法稳定性和设计参数 取值范围的分析表明,为了保证这种鲁棒滤波器存在,只要求离散系统中受不确定项影晌的 子系统二次稳定即可.实际算例显示了算法的有效性.Abstract: A new robust minimum variance filtering method for uncertain linear discrete-time systems is developed, which can guarantee minimizing bounded variance of filtering error. Unlike other robust filters, this filter only needs to solve two independent Riccati difference equations. So it is simple and effective to calculate and analyze. The existence of the new filter can be ensured by the quadratic stable of the sub-system that is affected by uncertain parameters. This result relaxes the assumption of quadratic stable of the whole system, which is required for some other robust filters. The practical calculation shows that the new robust Kalman filter is more effective.
-
Key words:
- Robust filtering /
- uncertain system /
- robust recognition /
- state estimation
-
计量
- 文章访问数: 2404
- HTML全文浏览量: 89
- PDF下载量: 967
- 被引次数: 0
下载:
