答案1
正如其他人已经提到的,这可以通过使用如下表格轻松实现:
\documentclass{beamer}
\usepackage{lmodern}
\usepackage{amsmath}
\usefonttheme{professionalfonts}
\begin{document}
\begin{frame}
\renewcommand{\arraystretch}{1.2}
\begin{tabular}{@{}ll@{}}
\textbf{Predict} & \\
Predicted state estimate & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\
Predicted covariance estimate & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\[.5em]
\textbf{Update} & \\
Innovation or measurement residual & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\
Innovation (or residual) covariance & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\
Near-optimal Kalman gain & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\
Updated state estimate & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$ \\
Updated covariance estimate & $\displaystyle\hat{\mathbf{x}}_{k-1|k-1}=f(\hat{\mathbf{x}}_{k|k-1},\mathbf{k}_{u-1})$
\end{tabular}
\end{frame}
\end{document}
答案2
使用简单的表格:
\documentclass{article}
\usepackage{amsmath}
\usepackage{array}
\begin{document}
\subsection*{Update}
\noindent
\begin{tabular}{@{} p{7cm} >{$}l<{$} @{}}
Innovation or measurement residual &
\overline{\mathbf{y}}_k = \mathbf{Z}_k - h(\mathbf{\hat{x}}_{k|k-1}) \\[5pt]
Innovation (or residual) covariance &
\mathbf{S}_k = \mathbf{H}_k \ldots
\end{tabular}
\end{document}