Matched Filtering

The cross-correlation function is used extensively in pattern recognition and signal detection. We know from Chapter 5 that projecting one signal onto another is a means of measuring how much of the second signal is present in the first. This can be used to ``detect'' the presence of known signals as components of more complicated signals. As a simple example, suppose we record $x(n)$ which we think consists of a signal $s(n)$ that we are looking for plus some additive measurement noise $e(n)$ . That is, we assume the signal model $x(n)=s(n)+e(n)$ . Then the projection of $x$ onto $s$ is (recalling §5.9.9)

$${\bf P}_s(x) \isdef \frac{\left<x,s\right>}{\Vert s\Vert^2} s = \frac{\left<s+e,s\right>}{\Vert s\Vert^2} s = s + \frac{\left<e,s\right>}{\Vert s\Vert^2} s = s + \frac{N}{\Vert s\Vert^2} {\hat r}_{se}(0)s \approx s$$

since the projection of random, zero-mean noise $e$ onto $s$ is small with probability one. Another term for this process is matched filtering. The impulse response of the ``matched filter'' for a real signal $s$ is given by $\hbox{\sc Flip}(s)$ .^8.11 By time-reversing $s$ , we transform the convolution implemented by filtering into a sliding cross-correlation operation between the input signal $x$ and the sought signal $s$ . (For complex known signals $s$ , the matched filter is $\hbox{\sc Flip}(\overline{s})$ .) We detect occurrences of $s$ in $x$ by detecting peaks in ${\hat r}_{sx}(l)$ .

In the same way that FFT convolution is faster than direct convolution (see Table 7.1), cross-correlation and matched filtering are generally carried out most efficiently using an FFT algorithm (Appendix A).