Autocorrelation

The autocorrelation of a signal $x$ is simply the cross-correlation of $x$ with itself:

$$(x \star x)(n) \isdefs \sum_m\overline{x(m)}x(m+n).$$ (3.24)

From the correlation theorem, we have

$$\zbox {(x \star x) \;\longleftrightarrow\;\vert X\vert^2}$$

Note that this definition of autocorrelation is appropriate for signals having finite support (nonzero over a finite number of samples). For infinite-energy (but finite-power) signals, such as stationary noise processes, we define the sample autocorrelation to include a normalization suitable for this case (see Chapter 6 and Appendix C).

From the autocorrelation theorem we have that a digital-filter impulse-response $h(n)$ is that of a lossless allpass filter [263] if and only if $h\star h = \delta \,\leftrightarrow\, 1$ . In other words, the autocorrelation of the impulse-response of every allpass filter is impulsive.