Aliasing in information processing and interpolating random signals by cubic spline

This paper considersthe problem of aliasing in information processing. The cubic spline method of interpolating the uniformly sampled signals and its effects on the autocorrelation estimation as well as the resulting spectral density function are studied by simulating random signals with known autocorrelation functions. Hence، by comparison of aliased and alias-free cases، indications are deduced from suspection to aliasing، especially in those situations that aliasing is present but the Nyquist frequency is not too far apart from the main peaks in the signal.