Cross Correlation Homework: Measuring Delay

AI Thread Summary
Cross-correlation is used to measure the similarity between transmitted signal x(t) and received signal y(t), revealing the time delay D. When computing the cross-correlation Ryx(l), a peak indicates the amount of delay, as the peak's position corresponds to the time shift. The relationship between the transmitted pulse and the received pulse allows for determining the distance to the reflecting object based on this delay. Techniques like the Discrete Fourier Transform (DFT) can be employed to compute cross-correlation efficiently. This understanding confirms that the method is valid for measuring time delay in signal processing.
EvLer
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Homework Statement


A signal x(t) is transmitted and received back as y(t) and sampled in the receiver, so we get a DT signal
y[n] = ax[n-D] + w[n], where w[n] is noise

baiscally i need to explain how we can measure the time delay by computing crosscorrelation Ryx(l).

My undestanding is:
cross-correlation measures how similar the signals are, so if we ccompute cross correlation of a signal with its reflected version, we will get shifted peaks, and the amount of shifting is the delay


Is this correct?
 
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Can you please solve it?
 
I realize this is a super old thread. But I was about to ask the exact same question. So I figure I would try bumping it up.

My understanding is very similar to EvLer's. I have a transmitted pulse x(t) and the received pulse is x(t). These are received by a receive and processed to determine the time delay. The distance from the reflecting object is the time delay.

Can anyone provide a little more information to make things clearer or confirm what is above in post or Evler's?

Thanks in advance!
 
Hey Evo8! :smile:

I can confirm what is in Evler's post.

The cross correlation of x and y has a peak at position D.
Cross correlation can typically be computed with a transform (DFT or other).
 
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