散射理论和线性最小二乘估计:第二部分:离散时间问题

B. Friedlander, T. Kailath, L. Ljung
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引用次数: 49

摘要

由Redheffer提出的散射理论中的某种“星积”形式也可以自然地适用于离散时间线性最小二乘估计问题。这种形式似乎提供了一种很好的方法来处理离散时间情况下的一些众所周知的代数复杂性,例如,时间和测量更新之间的区别,预测和过滤估计,等等。介绍了散射框架的其他几个应用,包括误差协方差的加倍公式、初始条件的变化公式、向后马尔可夫状态模型的方程,以及常数参数情况下chandrasekhar型方程的新推导。注意到离散时间和连续时间的区别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scattering theory and linear least squares estimation: Part II: Discrete-time problems
A certain "star-product" formalism in scattering theory as developed by Redheffer is shown to also be naturally applicable to discrete-time linear least-squares estimation problems. The formalism seems to provide a nice way of handling some of the well-known algebraic complications of the discrete-time case, e.g., the distinctions between time and measurement updates, predicted and filtered estimates, etc. Several other applications of the scattering framework are presented, including doubling formulas for the error covariance, a change of initial conditions formula, equations for a backwards Markov state model, and a new derivation of the Chandrasekhar-type equations for the constant parameter case. The differences between the discrete-time and continuous-time are noted.
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