written 8.4 years ago by | • modified 8.4 years ago |
Mumbai University > Electronics and Telecommunication > Sem5 > Random Signal Analysis
Marks: 10M
Year: May 2015
written 8.4 years ago by | • modified 8.4 years ago |
Mumbai University > Electronics and Telecommunication > Sem5 > Random Signal Analysis
Marks: 10M
Year: May 2015
written 8.4 years ago by | • modified 8.4 years ago |
Definition: If the process $\{X(t)\}$ is stationary either in the strict sense or in the wide sense, then $E\{X(t).X(t-τ)\}$is a function of τ, denoted by $R_xx (τ)$ or R(τ) or $R_x (τ)$. This function $R(τ)$ is called the autocorrelation function of the process $\{X(t)\}$
Properties:
Definition:
If $\{X(t)\}$ is a stationary process (either in a strict sense or wide sense) with auto correlation function $R(τ)$, then the Fourier transform of $R(τ)$ is called the power spectral density function of $\{X(t)\}$ and denoted as $S_{xx} (ω) or S_x (ω)$ Thus $S_x (ω)=∫_{-∞}^∞R(τ) e^{-iωτ} dτ$
Or $S_x (f)=∫_{-∞}^∞R(τ) e^{-i2πfτ} dτ$
Properties:
The value of the spectral density at zero frequency is equal to the total area under the graph of the auto correlation function
The mean square value of a wide sense stationary process is equal to the total area under the graph of the spectral density.
The spectral density function of a real random process is an even function i.e. $S_x (ω)=S_x (-ω)$
The Spectral density of a process {X(t)}, real or complex, is a real function of ω and non negative.
The spectral density and the autocorrelation function of a real WSS process form a Fourier Cosine transform pair
If $X_τ (ω)$ is the Fourier transform of the truncated random process defined as
$X_τ (t)=X(t) \ \ \ \ \ \ for |t| ≤T$
$ \ \ \ \ \ \ \ \ \ =0 \ \ \ \ \ \ \ \ \ \ \ for |t| \gt T$
where $\{X(t)\}$ is a real WSS process with power spectral density function $S(ω)$ then
$$S(ω)=\displaystyle\lim_{τ→∞}\frac{1}{2τ} E{|X_τ (ω) |^2}$$