Filter Implementation
Convolution and Filtering
The mathematical foundation of filtering is convolution. For a finite impulse response (FIR) filter, the outputy(k)of a filtering operation is the convolution of the input signalx(k)with the impulse responseh(k):
If the input signal is also of finite length, you can implement the filtering operation using the MATLAB®conv
函数。例如,过滤five-sample跑dom vector with a third-order averaging filter, you can storex(k)in a vectorx
,h(k)in a vectorh
, and convolve the two:
x = randn(5,1); h = [1 1 1 1]/4;% A third-order filter has length 4y = conv(h,x)
y = -0.3375 0.4213 0.6026 0.5868 1.1030 0.3443 0.1629 0.1787
y
是一个我ess than the sum of the lengths ofx
andh
.
Filters and Transfer Functions
The transfer function of a filter is the Z-transform of its impulse response. For an FIR filter, the Z-transform of the outputy,Y(z), is the product of the transfer function andX(z), the Z-transform of the inputx:
The polynomial coefficientsh(1),h(2), …,h(n+ 1)correspond to the coefficients of the impulse response of annth-order filter.
Note
The filter coefficient indices run from 1 to (n+ 1), rather than from 0 ton. This reflects the standard indexing scheme used for MATLAB vectors.
FIR filters are also called all-zero, nonrecursive, or moving-average (MA) filters.
For an infinite impulse response (IIR) filter, the transfer function is not a polynomial, but a rational function. The Z-transforms of the input and output signals are related by
whereb(i) anda(i) are the filter coefficients. In this case, the order of the filter is the maximum ofnandm. IIR filters withn= 0 are also called all-pole, recursive, or autoregressive (AR) filters. IIR filters with bothnandmgreater than zero are also called pole-zero, recursive, or autoregressive moving-average (ARMA) filters. The acronyms AR, MA, and ARMA are usually applied to filters associated with filtered stochastic processes.
Filtering with thefilter
Function
For IIR filters, the filtering operation is described not by a simple convolution, but by a difference equation that can be found from the transfer-function relation. Assume thata(1) = 1, move the denominator to the left side, and take the inverse Z-transform to obtain
In terms of current and past inputs, and past outputs,y(k) is
which is the standard time-domain representation of a digital filter. Starting withy(1) and assuming a causal system with zero initial conditions, the representation is equivalent to
To implement this filtering operation, you can use the MATLABfilter
函数。filter
stores the coefficients in two row vectors, one for the numerator and one for the denominator. For example, to solve the difference equation
you can use
b = 1; a = [1 -0.9]; y = filter(b,a,x);
filter
gives you as many output samples as there are input samples, that is, the length ofy
is the same as the length ofx
. If the first element ofais not 1, thenfilter
divides the coefficients bya(1) before implementing the difference equation.