假设我需要在算法中编号步骤内添加步骤，并且希望它们用字母自动标记。
我尝试使用\begin{description} \end{description}，但在某些时候我需要划分我的算法（长算法），并且当我想划分算法时使用描述环境会导致问题，有人可以帮忙吗？
\documentclass[a4paper,english,12pt,oneside]{book}
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\usepackage{algorithm}
\usepackage{algcompatible}
\usepackage{algorithmicx}
\usepackage{algpseudocode}
\usepackage{pifont}
%\usepackage[lite]{mtpro2}
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\usepackage[colorlinks=true,linkcolor=blue, citecolor=blue]{hyperref}
% define new commandes-------------------------------------------------------------------
\def\blank{\medskip\hrule\medskip}
%-----------------------------------------------------------------------------------------
\begin{document}
%\algstore{myalg}
% \end{algorithmic}
% \end{algorithm}
% \begin{algorithm}
% \begin{algorithmic} [1]
% \algrestore{myalg}
%-------------------------------------------------------------------------------------------------------
\begin{algorithm}
\caption{Square Root Cubature Kalman Filter SRCKF}
\textbf{Time update}
\begin{algorithmic}[1]
\State Factorize the state error covariance matrix
\begin{equation}
P_{k-1|k-1} = S_{k-1|k-1}S_{k-1|k-1}^T
\label{eq:Ref_3}
\end{equation}
\State Evaluate the cubature points (i=1,2,...,$m = 2n_x$)
\begin{equation}
X_{i,k-1|k-1} = \hat{x}_{k-1|k-1} + S_{i,k-1|k-1}\zeta_i
\label{eq:Ref_3}
\end{equation}
\State Evaluate the propagated cubature points through the process equation (i=1,2,...,$m = 2n_x$)
\begin{equation}
X_{i,k|k-1}^*=f(X_{i,k-1|k-1},u_{k-1})
\label{eq:Ref_3}
\end{equation}
\State Estimate the predicted state
\begin{equation}
\hat{x}_{k|k-1} = \frac{1}{m}\sum_{i=1}^m{X_{i,k|k-1}^*}Y
\label{eq:Ref_3}
\end{equation}
\State Estimate the predicted error covariance
\begin{equation}
P_{k|k-1}=\frac{1}{m}\sum_{i=1}^m{X_{i,k|k-1}^*X_{i,k|k-1}^{*T}}-\hat{x}_{k|k-1}\hat{x}_{k|k-1}^T+Q_{k-1}
\label{eq:Ref_3}
\end{equation}
\end{algorithmic}
\textbf{Measurement update}\\
\begin{algorithmic}[1]
\State Factorize the state error covariance matrix:
\begin{equation}
P_{k|k-1} = S_{k|k-1}S_{k|k-1}^T
\label{eq:Ref_3}
\end{equation}
\State Evaluate the cubature point (i=1,2,...,m)
\begin{equation}
X_{i,k|k-1} = \hat{x}_{k|k-1} + S_{k|k-1}\zeta_i
\label{eq:Ref_3}
\end{equation}
\State Evaluate the propagated cubature point through the measurement equation
\begin{equation}
Y_{i,k|k-1} = h(X_{i,k|k-1})
\label{eq:Ref_3}
\end{equation}
\begin{description}
\item[a.] Estimate the predicted measurement
\begin{equation}
\hat{y}_{k|k-1} = \frac{1}{m}\sum_{i=1}^m{Y_{i,k|k-1}}
\label{eq:Ref_3}
\end{equation}
\item[b.] Estimate the innovation covariance matrix
\begin{equation}
P_{yy,k|k-1} = \frac{1}{m}\sum_{i=1}^m{Y_{i,k|k-1}Y_{i,k|k-1}^T}-\hat{y}_{k|k-1}\hat{y}_{k|k-1}^T+R_k
\label{eq:Ref_3}
\end{equation}
\item[c.] Estimate the cross-covariance matrix
\begin{equation}
P_{xy,k|k-1}=\frac{1}{m}\sum_{i=1}^m{X_{i,k|k-1}Y_{i,k|k-1}^T}-\hat{x}_{k|k-1}\hat{y}_{k|k-1}^T
\label{eq:Ref_3}
\end{equation}
% split long alg-----------------------------------------------------------------------------------
\algstore{myalg}
\end{algorithmic}
\end{algorithm}
\clearpage
\begin{algorithm}
\ContinuedFloat
% \caption{Square Root Cubature Kalman Filter SRCKF (continued)}
\begin{algorithmic}
\algrestore{myalg}
%--------------------------------------------------------------------------------------------------
\item[d.] Estimate the Kalman gain
\begin{equation}
K_k=P_{xy,k|k-1}(P_{yy,k|k-1})^{-1}
\label{eq:Ref_4}
\end{equation}
\end{description}
\State Estimate the Updated state
\begin{equation}
\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_k(y_k-\hat{y}_{k|k-1})
\label{eq:Ref_2}
\end{equation}
\State Estimate the corresponding error covariance
\begin{equation}
P_{k|k}=P_{k|k-1}-K_kP_{yy,k|k-1}K^T_k
\label{eq:Ref_3}
\end{equation}
\end{algorithmic}
\end{algorithm}
\end{document}