Cross Correlation Functions: Advanced Applications & Conclusions

In summary, cross-correlation is a commonly used method for estimating coherence and time delay between two timeseries. It is often used in signal and image processing, and there are many resources available to learn more about it, such as papers in seismology and using MATLAB. It can also be thought of in terms of probability estimates or as a tool in deterministic problems.
  • #1
csopi
82
2
Hi.

Can somebody recommend a good book (advanced level) dealing with applications of cross correlation functions? I mean what kind of conclusions can be drawn etc?
 
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  • #2
Cross correlation functions are used a lot to estimate coherence and time delay between two timeseries. Under certain assumptions the maximum likelihood estimate of time delay uses the cross-correlation function. This is done all the time in signal and image processing. Carter is a name that comes to mind as someone who has published a lot in this area if you want to look in the literature. Not sure if there are any good books on this, though.

good luck,

jason
 
  • #3
I would recommend thinking of cross-correlation as an application of using inner product spaces. If that sounds a little advanced, no worries--it's not as scary as it sounds.

You can think of cross-correlation in terms of probability estimates (coherence and maximum likelihood) or in terms of deterministic problems. Seismology signal processing papers (many online, for free) make good teaching guides. In addition, seach terms for "matched filter". Sometimes an explicit application is great for teaching. If you have access to MATLAB, this also makes for a good teaching tool. You can use xcor to your hearts content. Finally, if you use cross-correlation with it's Fourier Transform Theorem, you will find the properties more useful. It is a great tool! Good luck.
 

Related to Cross Correlation Functions: Advanced Applications & Conclusions

1. What is a cross correlation function?

A cross correlation function is a statistical tool used to measure the similarity between two signals or data sets. It quantifies the degree to which the two signals are linearly related to each other.

2. How is a cross correlation function calculated?

The calculation of a cross correlation function involves multiplying one signal by a time-shifted version of the other signal, and then summing the resulting values. This process is repeated for different time shifts, creating a plot of correlation values over time.

3. What are some common applications of cross correlation functions?

Cross correlation functions are commonly used in signal processing, time series analysis, and pattern recognition. They can also be used to study relationships between different data sets, such as weather and crop yields, or economic indicators and stock prices.

4. What is the difference between cross correlation and autocorrelation?

Autocorrelation measures the similarity between different points in the same signal, while cross correlation measures the similarity between two different signals. In other words, autocorrelation looks for patterns within a single signal, while cross correlation looks for patterns between two signals.

5. Are there any limitations to using cross correlation functions?

Yes, there are some limitations to using cross correlation functions. They are most effective when looking at linear relationships between signals, and may not accurately capture non-linear relationships. Additionally, cross correlation can be affected by noise in the data and may require careful interpretation.

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