Convolution involves finding output of a Linear Time Invariant system. Correlation provides degree of similarity between two signals.
The user inputs the signals x(n) and h(n).
To find convolution, the length of the signals are calculated to be L and M respectively. Thus, the length of output signal is given by
1. L= N + M - 1 For linear convolution
2. L= Max(N, M) For circular convolution
3. L >= N + M - 1 For linear convolution by circular convolution
To find correlation, the output y(n) is an even signal i.e. y(n)=y(-n).
1. If the input signals are delayed, the output does not change.
1. If the input signals are delayed, the output does not change.
2. If we try cross correlation of a signal with its delayed self, we get a result that is an advanced signal from the auto correlation output.
Any comparison between these two methods?
ReplyDeleteThese are totally different techniques with specif application
DeleteBoth methods convolution and correlation are used for different applications as mentioned in the first line of the blog. Thanks!
ReplyDeleteCorrelation has application in radar, speech recognition etc.
ReplyDeleteYes, correlation can be used to check the similarity between two signals
DeleteThese are important operations in signal processing
ReplyDeleteYes, these operations have a lot of applications
DeleteConvolution is used to find output.
ReplyDeleteYes, convolution is obtained by multiplication in transorm domain
DeleteYou're right, convolution is simpler in transfer domain than in time domain
DeleteCorrelation is used to find degree of similarity
ReplyDeleteYes, Correlation has applications in radar, speech recognition etc.
DeleteBasic code can be enhanced by introducing new functions and new operations can be performed
ReplyDeleteCorrelation is used in Radar system for obtaining target location
ReplyDeleteConvolution can be used to compute the output of a signal
ReplyDeleteCorrelation is used for signal comparison applications.
ReplyDelete