content(symbol): 噪声方差全文统一记为 \sigma（去 n 与 ^2）

B:10 早已声明"\sigma 表示方差"，sec1/D 遵守，多星侧却用 \sigma_{n,\ell}^2
偏离。统一回归该约定：\sigma_{n,\ell}^2->\sigma_\ell、\sigma_n^2->\sigma
（去 n 与 ^2）；推导中的方差平方 \sigma_{n,\ell}^4->\sigma_\ell^2；白化
标准差 1/\sigma_{n,\ell}->1/\sqrt{\sigma_\ell}（E:765）；E 共同方差情形裸
\sigma^2->\sigma。D 里 \sigma=方差的幂次 \sigma^2/\sigma^4 本就正确、保留。
清理替换产生的循环定义（sec1/D）。如此推导上标即方差的真实幂次，不与
"方差自带 ^2"冲突。

parts/C-classicalDoa.tex

@@ -181,7 +181,7 @@ e^{-j 2\pi \frac{d}{\lambda_{\mathrm{c}}}(M-1)\sin\theta_k}
 %
 where $d$ denotes the inter-element spacing and $\lambda_{\mathrm{c}}$ is the carrier wavelength.
 
-Assuming that the source signals are mutually uncorrelated and that the noise is spatially white with variance $\sigma_n^2$, the array covariance matrix is
+Assuming that the source signals are mutually uncorrelated and that the noise is spatially white with variance $\sigma$, the array covariance matrix is
 %
 \begin{equation}
 \mathbf{R}_y
@@ -190,7 +190,7 @@ Assuming that the source signals are mutually uncorrelated and that the noise is
 =
 \mathbf{A}(\boldsymbol{\theta}) \mathbf{R}_s \mathbf{A}^{\Herm}(\boldsymbol{\theta})
 +
-\sigma_n^2 \mathbf{I}_M,
+\sigma \mathbf{I}_M,
 \label{eq:music_covariance}
 \end{equation}
 %
@@ -543,7 +543,7 @@ where $\mathbf{n}_{\ell} \triangleq \vect(\mathbf{N}_{\ell})$ and
 %
 is the spatio-temporal steering vector, with $\otimes$ denoting the Kronecker product. Hence, the joint angle-Doppler estimation problem can be recast as a standard subspace estimation problem in the augmented spatio-temporal domain.
 
-Assuming that the source signals are mutually uncorrelated and the noise is spatially and temporally white with variance $\sigma_n^2$, the covariance matrix of $\mathbf{y}_{\ell}$ is given by
+Assuming that the source signals are mutually uncorrelated and the noise is spatially and temporally white with variance $\sigma$, the covariance matrix of $\mathbf{y}_{\ell}$ is given by
 %
 \begin{equation}
 \mathbf{R}_y
@@ -552,7 +552,7 @@ Assuming that the source signals are mutually uncorrelated and the noise is spat
 =
 \mathbf{B}_{\mathrm{st}}\mathbf{R}_{\zeta}\mathbf{B}_{\mathrm{st}}^\Herm
 +
-\sigma_n^2 \mathbf{I}_{MT},
+\sigma \mathbf{I}_{MT},
 \label{eq:covariance_st}
 \end{equation}
 %