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    Convolution

    What is the differance between convolution and correlation

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    Re: Convolution

    Quote Originally Posted by sundarmeenakshi
    What is the differance between convolution and correlation
    digital convolution is called correlation


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    •   Alt26th January 2008, 09:52

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    Re: Convolution

    correlation is used to compare the similarity of two signals, the result is a signal that shows this similarity and reaches its maximum at the time when the two signals match best. correlation can be used to measure the delay of a certain system.

    convolution is used to compute the output of a certain linear system when a certain input signal is applied to it. this is done by applying convolution between the input signal and the impulse response of the system


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    •   Alt26th January 2008, 13:30

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    Re: Convolution

    From mathematical point of view convolution of two function is:

    x(t)*y(t)=∫x(Θ)y(t-Θ)dΘ

    and correlation is

    E[x(t)y(t)]=∫x(Θ)y(t+Θ)dΘ


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    •   Alt27th January 2008, 07:59

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    Re: Convolution

    Convolution is the operation / transformation that governs the i/p - o/p relationship in a LTI system.

    Correlation is a statistical measure of how similar two waveforms or random processes are.

    In the case of a matched filter, correlation and convolution becomes the same.


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    Convolution

    convolution is specifically used for the multiplication of continous and discrete time signal in time domain and valid for LTI system but correlation is matching and comparision of signal and there are two types auto and cross correlation and its example in missile imaging

    Added after 22 seconds:

    convolution is specifically used for the multiplication of continous and discrete time signal in time domain and valid for LTI system but correlation is matching and comparision of signal and there are two types auto and cross correlation and its example in missile imaging and using correlators and matched filters for taht



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    Re: Convolution

    Hi,
    Convolution of two signals can be interpreted graphically as folows:
    supose you take convolution between x(t) and y(t). Then in t domain just reverse the signal y(t) i.e. plot the signal y(-t). Now translate the signal y(-t) from -∞ to Γ. The overlap area is the convolution between x(t) and y(t). at the point Γ.

    On the otherhand the correlation is the same thing, only thing is that, you dont have to invert the signal y(t) in t domain. Translate y(t) itself from -∞ to Γ. The overlap area gives the correlation between these two signals.



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    Convolution

    Completly agree with asoom



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    Convolution

    Correlation + Shifting = Convolution

    This is the difference between the both.



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