fix(symbol): \arg\max/\arg\min 改用 \argmax/\argmin 算子

preamble 已用 \DeclareMathOperator*{\argmax}{arg\,max} 定义算子，应整体使用
而非 \arg\max（后者下标只落在 max 下、且非整体算子）。全文 12 处统一为
\argmax/\argmin。

parts/C-classicalDoa.tex

@@ -124,7 +124,7 @@ The \gls{doa} estimates are then obtained by locating the $K$ dominant peaks of
 \begin{equation}
 \hat{\theta}_k
 =
-\arg\max_{\theta}
+\argmax_{\theta}
 \widehat{P}_{\mathrm{MVDR}}(\theta),
 \qquad k=1,\dots,K.
 \label{eq:mvdr_estimation}
@@ -256,7 +256,7 @@ The \gls{doa} estimates are then obtained by locating the $K$ dominant peaks of
 \begin{equation}
 \hat{\theta}_k
 =
-\arg\max_{\theta}
+\argmax_{\theta}
 P_{\mathrm{MUSIC}}(\theta),
 \qquad k=1,\dots,K.
 \label{eq:music_estimation}
@@ -427,7 +427,7 @@ In practice, $\bm{\Psi}$ can be estimated by solving the least-squares problem
 \begin{equation}
 \widehat{\bm{\Psi}}
 =
-\arg\min_{\bm{\Psi}}
+\argmin_{\bm{\Psi}}
 \left\|
 \mathbf{U}_{s,2} - \mathbf{U}_{s,1}\bm{\Psi}
 \right\|_{\mathrm{F}}^2,
@@ -619,7 +619,7 @@ The joint estimates of \gls{doa} and Doppler are then obtained by searching for
 \begin{equation}
 (\hat{\theta}_k,\hat{\nu}_k)
 =
-\arg\max_{\theta,\nu}
+\argmax_{\theta,\nu}
 P_{\mathrm{ST\text{-}MUSIC}}(\theta,\nu),
 \qquad k=1,\dots,K.
 \label{eq:st_music_estimation}

parts/sec3_multiSatSingleUser.tex

@@ -224,7 +224,7 @@
 \begin{equation}
   \widehat{\boldsymbol\eta}
   =
-  \arg\min_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
+  \argmin_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}
   M_\ell T
   \log \mathcal E_\ell(\boldsymbol\xi,\nu_{\mathrm{ref}}),
@@ -242,7 +242,7 @@
 \begin{equation}
   \widehat{\boldsymbol\eta}
   =
-  \arg\max_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
+  \argmax_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}
   \frac{1}{\sigma_\ell}
   \frac{\left|\mathbf g_\ell^\Herm\tilde{\mathbf y}_\ell\right|^2}

parts/sec4_multiSatMultiUser.tex

@@ -152,7 +152,7 @@ $\boldsymbol\Xi\triangleq[\boldsymbol\xi_1,\ldots,\boldsymbol\xi_K]$，$\boldsym
   \label{eq:sec4_concentrated}
   \widehat{\boldsymbol\eta}_{\mathrm{mu}}
   =
-  \arg\min_{\boldsymbol\Xi,\boldsymbol\nu_{\mathrm{ref}}}
+  \argmin_{\boldsymbol\Xi,\boldsymbol\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}
   M_\ell T
   \log
@@ -170,7 +170,7 @@ $\boldsymbol\Xi\triangleq[\boldsymbol\xi_1,\ldots,\boldsymbol\xi_K]$，$\boldsym
 \begin{equation}
   \widehat{\boldsymbol\eta}_{\mathrm{mu}}
   =
-  \arg\max_{\boldsymbol\Xi,\boldsymbol\nu_{\mathrm{ref}}}
+  \argmax_{\boldsymbol\Xi,\boldsymbol\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}
   \frac{1}{\sigma_\ell}
   \tilde{\mathbf y}_\ell^\Herm

parts/sec6_multiFrameCp.tex

@@ -176,7 +176,7 @@ CP 模型的干扰参数向量为
 \begin{equation}
   \widehat{\boldsymbol\eta}
   =
-  \arg\min_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
+  \argmin_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
   \min_{\dot\nu_{\mathrm{ref}},\{\chi_\ell\}}
   \sum_{\ell=0}^{L-1}M_\ell PT\log \mathcal R^{\mathrm{cp,str}}_\ell.
 \end{equation}
@@ -191,7 +191,7 @@ CP 模型的干扰参数向量为
 \begin{equation}
   \widehat{\boldsymbol\eta}_{\mathrm{dyn,rel}}
   =
-  \arg\min_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
+  \argmin_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
   \min_{\dot\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}M_\ell PT\log \mathcal R^{\mathrm{cp,rel}}_\ell.
 \end{equation}
@@ -201,7 +201,7 @@ CP 模型的干扰参数向量为
 \begin{equation}
   \widehat{\boldsymbol\eta}_{\mathrm{dyn,ip}}
   =
-  \arg\min_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
+  \argmin_{\boldsymbol\xi,\nu_{\mathrm{ref}}}
   \min_{\dot\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}M_\ell PT\log \mathcal R^{\mathrm{ip}}_\ell.
 \end{equation}

parts/sec7_userPositioning.tex

@@ -81,7 +81,7 @@
 %
 \begin{equation}
   \widehat{\boldsymbol\eta}_{\mathrm{pos}}
-  =\arg\min_{\mathbf p,\nu_{\mathrm{ref}}}
+  =\argmin_{\mathbf p,\nu_{\mathrm{ref}}}
   \sum_{\ell=0}^{L-1}M_\ell T\log\mathcal E_\ell(\mathbf p,\nu_{\mathrm{ref}}),
   \qquad
   \mathcal E_\ell\triangleq\|\tilde{\mathbf y}_\ell\|_2^2-\frac{\big|\mathbf g_\ell^\Herm\tilde{\mathbf y}_\ell\big|^2}{\|\mathbf g_\ell\|_2^2}.