Mixed H2/H-infinity Approach to Robust Estimation and Adaptive Filtering

鲁棒估计和自适应滤波的混合 H2/H-无穷大方法

• 批准号: 9529325
• 负责人: Thomas Kailath
• 金额: $ 22.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-15 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9529325&HistoricalAwards=false
• 关键词: Mixed; H2; infinity; Approach; Robust

9529325 Kailath The project has two different but related objectives. The first is the study of mixed H-2/H-inf control and estimation problems; the second is to develop new software that will enable graduate students and researchers to run realistic projects that will call upon them to synthesize knowledge from different areas of statistical signal processing and numerical analysis. Classical H-2 methods (such as Wiener filtering and Kalman filtering) require knowledge of second-order statistics of the exogeneous signals and yield estimators that have best average performance. On the other hand, the more recent H-inf (or so called minimax) estimation methods require no statistical knowledge of the exogeneous signals and yield conservative solutions. The major goals of this proposal are (1) to develop, as completely as possible, an analytical characterization of the solution of mixed H-2/H-inf problem, (2) to obtain a through understanding of the tradeoffs between the average and worst-case performances, (3) to seek recursive implementations of the optimal, (4) to provide efficient numerical algorithms. ***

9529325 Kailath该项目有两个不同但相关的目标。第一个是研究H-2/H-INF混合控制和估计问题；第二个是开发新的软件，使研究生和研究人员能够开展现实的项目，要求他们综合统计信号处理和数值分析不同领域的知识。经典的H-2方法(如维纳滤波和卡尔曼滤波)需要知道外源信号的二阶统计量和具有最佳平均性能的产量估计器。另一方面，最近的H-inf(或所谓的极小极大)估计方法不需要外源信号的统计知识，并且得到保守的解。这一建议的主要目标是：(1)尽可能完整地发展混合H-2/H-INF问题解的分析特征；(2)通过对平均性能和最差性能之间的权衡的理解获得一个完整的解；(3)寻求最优性能的递归实现；(4)提供有效的数值算法。***