Finding power spectral density (ESD first)

In summary: Since the integral of a constant is just the value of the constant times the interval of integration, we have:Sg(w) = 1/2(t + 0) + 1/2\int_{-\infty}^{\infty}cos2wt dt = 1/2(t) + 1/2(0) = 1/2tTherefore, the power spectral density Sg(w) of the signal g(t) = cos wt is 1
  • #1
thomas49th
655
0

Homework Statement


Find the Power Spectral Density Sg(w) of the power signal g(t) = cos wt.
(Hint: Compute the autocorrelation function first, and then use the prop-
erty Rg(T ) <=> Sg(w))

Homework Equations



Well, if I take the hint's path I need to calculate the ESD first

ESD(T) = [tex]\int_{-\infty}^{\infty}g(t)g(t+T)dt[/tex]

The Attempt at a Solution



Firstly ESD(T) - why does the energy spectral density change as T changes. Isn't the energy spectral density the amount of energy that each frequency "requires" out of the signal? Do I simply integrate

[tex]\int_{-\infty}^{\infty}cos(w_{0}t) \cdot cos(w_{0}t+T)dt[/tex]

Spent ages reading over this and don't understand it very well!

Thanks
Thomas
 
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  • #2

Hello Thomas,

Thank you for your question. The energy spectral density (ESD) is a measure of the energy contained in a signal at different frequencies. As the signal g(t) = cos wt is a power signal, the ESD will give us the power spectral density (PSD) instead. The PSD is a measure of the power contained in a signal at different frequencies.

To calculate the PSD, we first need to calculate the autocorrelation function Rg(T) of the signal g(t). This can be done by using the definition of the autocorrelation function:

Rg(T) = \int_{-\infty}^{\infty}g(t)g(t+T)dt

Substituting g(t) = cos wt into the integral, we get:

Rg(T) = \int_{-\infty}^{\infty}cos wt \cdot cos(wt+T)dt

Using the trigonometric identity cos a cos b = 1/2(cos(a-b) + cos(a+b)), we can simplify the integral to:

Rg(T) = 1/2\int_{-\infty}^{\infty}cos wt \cdot cos(wt+T)dt + 1/2\int_{-\infty}^{\infty}cos wt \cdot cos(wt-T)dt

Since the cosine function is an even function, the second integral will be equal to the first integral with T replaced by -T. Therefore, we can rewrite the integral as:

Rg(T) = \int_{-\infty}^{\infty}cos wt \cdot cos(wt+T)dt

Now, using the property Rg(T) <=> Sg(w), we can say that the PSD Sg(w) of the signal g(t) is equal to the autocorrelation function Rg(T) evaluated at T = 0. Therefore, we have:

Sg(w) = Rg(0) = \int_{-\infty}^{\infty}cos wt \cdot cos(wt+0)dt = \int_{-\infty}^{\infty}cos^{2}wt dt

Using the trigonometric identity cos^{2} a = 1/2(1 + cos2a), we can simplify the integral to:

Sg(w) = 1/2\int_{-\infty
 

1. What is power spectral density (PSD)?

Power spectral density (PSD) is a measure of the distribution of power across different frequencies in a signal or time series data. It provides information about the strength and frequency components of a signal and is often used to analyze the frequency content of a system or process.

2. How is PSD calculated?

PSD is typically calculated using a mathematical technique called the Fourier transform, which decomposes a signal into its frequency components. The squared magnitude of the Fourier transform is then used to calculate the PSD, which represents the power at each frequency.

3. What is the significance of finding PSD?

Finding PSD can provide valuable insights into the underlying processes or systems that generate a signal. It can help identify dominant frequencies, patterns, or anomalies in the data and can aid in the design and optimization of systems or processes.

4. How is PSD used in real-world applications?

PSD is used in a wide range of fields, including signal processing, communications, physics, and engineering. It is commonly used in the analysis of time series data, such as in the study of weather patterns, stock market fluctuations, and brain activity. It is also used in the design and testing of electronic circuits and in vibration analysis to detect faults in machinery.

5. Are there any limitations to using PSD?

While PSD can provide valuable information about a signal, it does have some limitations. It assumes that the signal is stationary, meaning that its statistical properties do not change over time. In addition, PSD does not provide information about the phase of a signal, which can be important in some applications. It is also affected by noise and other sources of variability in the data.

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