Introduction to Filtering
9.3.1 Introduction to
In the field of signal processing the design of digital signal filters involves the process of suppressing certain frequencies and boosting others. A simplified filter model is
where the input signal is is modified to obtain the output signal using the recursion formula
The implementation of (9-23) is straightforward and only requires starting values, then is obtained by simple iteration. Since the signals must have a starting point, it is common to require that and for . We emphasize this concept by making the following definition.
Definition 9.3 (Causal Sequence) Given the the input and output sequences. If and for , the sequence is said to be causal.
Given the causal sequence ,
it is easy to calculate the solution
to (9-23). Use the fact that
these sequences are causal:
The general iterative step is
9.3.2 The Basic
The following three simplified basic filters serve as illustrations.
(i) Zeroing Out Filter , (note that ).
(ii) Boosting Up Filter , (note that ).
(iii) Combination Filter .
for these model filters has the
following general form
where the z-transforms of the input and output sequences are and , respectively. In the previous section we mentioned that the general solution to a homogeneous difference equation is stable only if the zeros of the characteristic equation lie inside the unit circle. Similarly, if a filter is stable then the poles of the transfer function must all lie inside the unit circle.
Before developing the general theory, we
would like to investigate the amplitude
when the input signal is a linear combination
and . The
amplitude response for the frequency
uses the complex unit signal ,
and is defined to be
The formula for will be rigorously explained after a few introductory examples.
Example 9.21. Given
the filter .
9.21 (a). Show that it is a zeroing out filter for the signals and and calculate the amplitude response .
9.21 (b). Calculate the amplitude responses and investigate the the filtered signal for .
9.21 (c). Calculate the amplitude responses and investigate the the filtered signal for .
Figure 9.4. The amplitude response for .
Figure 9.5. The input and output .
Figure 9.6. The input and output .
Explore Solution 9.21.
Example 9.22. Given the filter .
9.22 (a). Show that it is a boosting up filter for the signals and and calculate the amplitude response .
9.22 (b). Calculate the amplitude responses and investigate the the filtered signal for .
Figure 9.7. The amplitude response for .
Figure 9.8. The input and output .
Explore Solution 9.22.
General Filter Equation
The general form of a order filter difference equation is
where and are constants. Note carefully that the terms involved are of the form and where and , which makes these terms time delayed. The compact form of writing the difference equation is
where the input signal is modified to obtain the output signal using the recursion formula
The portion will "zero out" signals and will "boost up" signals.
called the recursion equation and the recursion coefficients are
and . It
explicitly shows that the present output is
a function of the past values , for
the present input , and
the previous inputs for . The
sequences can be regarded as signals and they are zero for negative
indices. With this information we can now define the
general formula for the transfer function . Using
the time delayed-shift property for causal sequences and taking the
z-transform of each term in (9-31), we
We can factor out
of the summations and write this in an equivalent form
From equation (9-33) we obtain
which leads to the following important definition.
(Transfer Function) The
transfer function corresponding to the
order difference equation (8) is given by
Formula (9-34) is the transfer function for an infinite impulse response filter (IIR filter). In the special case when the denominator is unity it becomes the transfer function for a finite impulse response filter (FIR filter).
Definition 9.5 (Unit-Sample Response) The sequence corresponding to the transfer function is called the unit-sample response.
Theorem 9.6 (Output
Response) The output
a the filter (10) given an input signal is
given by the inverse z-transformation
and in convolution form it is given by
Another important use of the transfer
function is to study how a filter affects various
frequencies. In practice, a continuous time signal is
sampled at a frequency that
is at least twice the highest input signal frequency to avoid
frequency fold-over, or aliasing. That is because the Fourier
transform of a sampled signal is periodic with
period , though
we will not prove this here. Aliasing prevents accurate
recovery of the original signal from its samples.
Now it can be shown that the argument of the Fourier transform maps onto the z-plane unit circle via the formula
(9-37) , where is called the normalized frequency.
Therefore the z-transform evaluated on the unit circle is also periodic, except with period .
Definition 9.6 (Amplitude
is defined to be the magnitude of the transfer function evaluated at
the complex unit signal . The
(9-38) over the interval .
The fundamental theorem of algebra implies that the numerator has roots (called zeros) and the denominator has roots (called poles). The zeros may be chosen in conjugate pairs on the unit circle and for . For stability, all the poles must inside the unit circle and for . Furthermore, the poles are chosen to be real numbers and/or in conjugate pairs. This will guarantee that the recursion coefficients are all real numbers. IIR filters may be all pole or zero-pole and stability is a concern; FIR filters and all zero-filters are always stable.
In practice recursion formula (10) is used to calculate the output signal. However, digital filter design is based on the above theory. One starts by selecting the location of zeros and poles corresponding to filter design requirements and constructing the transfer function . Since the coefficients in are real, all zeros and poles having an imaginary component must occur in conjugate pairs. Then the recursion coefficients are identified in (13) and used in (10) to write the recursive filter. Both the numerator and denominator of can be factored into quadratic factors with real coefficients and possibly one or two linear factors with real coefficients. The following principles are used to construct .
(i) Zeroing Out
To filter out the signals and , use factors of the form
if , and
in the numerator of . They will contribute to the term
(ii) Boosting Up
To amplify the signals and , use factors of the form
if and , and
if and , and
if and ,
in the denominator of . They will contribute to the term
To attenuate the signals and , use factors of the form
if and , and
if and .
if and ,
is a special case that attenuates low frequency signals. These factors will contribute to the term (9-42).
(iv) Combination of
The transfer function could have a zero or pole at the origin, but this has no net effect on the output signal. The other zeros and poles determine the nature of the filter. A conjugate pair of zeros of on the unit circle will "zero-out" the signals and . If the conjugate pair of zeros of will attenuate the signals and , and the conjugate pair of poles of will amplify the signals and . It is useful to plot the location of the zeros and poles and notice their magnitude and argument. As a general rule, zeros are used to attenuate signals and poles are used to amplify signals. The primary goal of filter design is to construct so that the amplitude response has a desired shape. The following examples have been chosen to illustrate these concepts. Books on digital signal filter design will explain the process in detail.
Example 9.23 (a). The filter is designed to zero out .
9.23 (b). The moving average filter is designed to zero out .
Figure 9.9. Amplitude response , and zero-pole plot for .
Explore Solution 9.23.
Example 9.24. The
moving average filter
is designed to zero out .
Figure 9.10. Amplitude response , and zero-pole plot for .
Explore Solution 9.24.
Example 9.25 (a). Design a filter with poles for boosting up signals near and .
9.25 (b). Include the additional pole at to the filter design in (a) so that it also boosts up low frequency signals.
Figure 9.11. Amplitude response , and zero-pole plot for .
Figure 9.12. Amplitude response , and zero-pole plot for .
Explore Solution 9.25.
Example 9.26. Design a combination filter using the zeros for zeroing out and poles boosting up some of the low frequencies.
Figure 9.13. Amplitude response , and zero-pole plot for .
Explore Solution 9.26.
Low Frequency Filter
The previous examples emphasize high frequency filters. To boost up high frequencies place the poles near the higher frequencies. The following example is similar to Example 9.25. The poles have been changed from to .
Extra Example 1 (a). Design a filter with poles for boosting up signals near and .
1 (b). Include the additional pole at to the filter design in (a) so that it also boosts up high frequency signals.
Explore Extra Solution 1.
Band Pass Filter
Poles can be placed near frequencies to boost them up and zeros for attenuating the other frequencies.
Extra Example 2. Design a combination filter boosting up some of the low frequencies where and attenuating frequencies and .
Explore Extra Solution 2.
A signal processing engineer uses complex analysis to construct filters with the desired amplitude and phase response characteristics. Finite impulse response (FIR) filters have only zeros, whereas infinite impulse response (IIR) filters have poles and may have zeros as well. The area of filter design involves many types, such as: low pass, high pass, all pass, band pass and band stop. Special forms of such filters include, but are not limited to Bessel, Butterworth, Chebyshev, Gaussian, moving average, single pole, Remez, etc. More information about filter design can be found in books on digital signal processing.
Algorithm (Analysis of
Filters) The following subroutine will analyze
the transfer function for . The
The functions and must be defined.
Mathematica Subroutine (Analysis of Filters)
Exercises for Section 9.3. Digital Signal Filters
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This material is coordinated with our book Complex Analysis for Mathematics and Engineering.
(c) 2012 John H. Mathews, Russell W. Howell