Show pageOld revisionsBacklinksExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Stationarity ====== ===== Strict-sense stationarity ===== A random process is strict-sense stationary if the joint density function of the random variables obtained by sampling that process is invariant under arbitrary time shifts: $$ f_{X(t_1), \dots X(t_\ell)}(x_1, \dots, x_\ell) = f_{X(t_1 + \alpha), \dots X(t_\ell + \alpha)}(x_1, \dots, x_\ell) $$ ===== Wide-sense stationarity ===== A random process is strict-sense stationarity if: * The mean $\mu_X(t)$ is invariant with time: $$ \mu_X(t) = \mu_X $$ * The autocorrelation $R_{XX}(t_1, t_2)$ and autocovariance $C_{XX}(t_1, t_2)$ only depend on the time difference $(t_1 - t_2)$. $$ R_{XX}(t_1, t_2) = R_{XX}(t_1 - t_2) $$ $$ C_{XX}(t_1, t_2) = C_{XX}(t_1 - t_2) $$ Strict-sense stationarity implies wide-sense stationarity. ==== Properties of WSS correlation/covariance functions ==== Symmetry properties: $$ R_{xx}(\tau) = R_{xx}(-\tau) $$ $$ C_{xx}(\tau) = C_{xx}(-\tau) $$ $$ R_{xy}(\tau) = R_{yx}(-\tau) $$ $$ C_{xy}(\tau) = C_{yx}(-\tau) $$ kb/stationarity.txt Last modified: 2024-04-30 04:03by 127.0.0.1