where ``'' denotes the convolution operator. (See 6.4 for an elementary introduction to convolution.)
If the above equation is not obvious, here is how it is built up intuitively. Imagine as a 1 in the midst of an infinite string of 0s. Now think of as the same pattern shifted over to the right by samples. Next multiply by , which plucks out the sample and surrounds it on both sides by 0's. An example collection of waveforms for the case is shown in Fig.5.4a. Now, sum over all , bringing together the samples of , to obtain . Figure 5.4b shows the result of this addition for the sequences in Fig.5.4a. Thus, any signal may be expressed as a weighted sum of shifted impulses.
Equation (5.4) expresses a signal as a linear combination (or weighted sum) of impulses. That is, each sample may be viewed as an impulse at some amplitude and time. As we have already seen, each impulse (sample) arriving at the filter's input will cause the filter to produce an impulse response. If another impulse arrives at the filter's input before the first impulse response has died away, then the impulse response for both impulses will superimpose (add together sample by sample). More generally, since the input is a linear combination of impulses, the output is the same linear combination of impulse responses. This is a direct consequence of the superposition principle which holds for any LTI filter.
We repeat this in more precise terms. First linearity is used and then time-invariance is invoked. Using the form of the general linear filter in Eq.(4.2), and the definition of linearity, Eq.(4.3) and Eq.(4.5), we can express the output of any linear (and possibly time-varying) filter by
where we have written to denote the filter response at time to an impulse which occurred at time . If we are to be completely rigorous mathematically, certain ``smoothness'' restrictions must be placed on the linear operator in order that it may be distributed inside the infinite summation . However, practically useful filters of the form of Eq.(5.1) satisfy these restrictions. If in addition to being linear, the filter is time-invariant, then , which allows us to write
This states that the filter output is the convolution of the input with the filter impulse response .
The infinite sum in Eq.(5.5) can be replaced by more typical practical limits. By choosing time 0 as the beginning of the signal, we may define to be 0 for so that the lower summation limit of can be replaced by 0. Also, if the filter is causal, we have for , so the upper summation limit can be written as instead of . Thus, the convolution representation of a linear, time-invariant, causal digital filter is given by
Since the above equation is a convolution, and since convolution is commutative (i.e., ), we can rewrite it as
We have shown that the output of any LTI filter may be calculated by convolving the input with the impulse response . It is instructive to compare this method of filter implementation to the use of difference equations, Eq.(5.1). If there is no feedback (no coefficients in Eq.(5.1)), then the difference equation and the convolution formula are essentially identical, as shown in the next section. For recursive filters, we can convert the difference equation into a convolution by calculating the filter impulse response. However, this can be rather tedious, since with nonzero feedback coefficients the impulse response generally lasts forever. Of course, for stable filters the response is infinite only in theory; in practice, one may truncate the response after an appropriate length of time, such as after it falls below the quantization noise level due to round-off error.
Finite Impulse Response Digital Filters
Implications of Linear-Time-Invariance