In this section, we will find the frequency response of the simplest lowpass filter
using simulated sine-wave analysis carried out by a matlab program. This numerical approach complements the analytical approach followed in §1.3. Figure 2.3 gives a listing of the main script which invokes the sine-wave analysis function swanal listed in Fig.. The plotting/printing utilities swanalmainplot and swanalplot are listed in Appendix J starting at §J.12.
% swanalmain.m - matlab program for simulated sinewave % analysis on the simplest lowpass filter: % % y(n) = x(n)+x(n-1)} B = [1,1]; % filter feedforward coefficients A = 1; % filter feedback coefficients (none) N = 10; % number of sinusoidal test frequencies fs = 1; % sampling rate in Hz (arbitrary) T = 1/fs; % sampling interval in seconds fmax = fs/2; % highest frequency to look at df = fmax/(N-1);% spacing between frequencies f = 0:df:fmax; % sampled frequency axis tmax = 1/df; % 1 cycle at lowest non-dc freq t = 0:T:tmax; % sampled time axis % [gains,phases] = swanal(t,f,fs,B,A); % sinewave analysis swanal; % configured for ease of variable inspection plotfile = '../eps/swanalmain.eps'; swanalmainplot; % final plots and comparison to theory
In the swanal function (Fig.), test sinusoids are generated by the line
s = ampin * cos(2*pi*f(k)*t + phasein);where amplitude, frequency (Hz), and phase (radians) of the sinusoid are given be ampin, f(k), and phasein, respectively. As discussed in §1.3, assuming linearity and time-invariance (LTI) allows us to set
ampin = 1; % input signal amplitude phasein = 0; % input signal phasewithout loss of generality. (Note that we must also assume the filter is LTI for sine-wave analysis to be a general method for characterizing the filter's response.) The test sinusoid is passed through the digital filter by the line
y = filter(B,A,s); % run s through the filterproducing the output signal in vector y. For this example (the simplest lowpass filter), the filter coefficients are simply
B = [1,1]; % filter feedforward coefficients A = 1; % filter feedback coefficients (none)The coefficient A(1) is technically a coefficient on the output signal itself, and it should always be normalized to 1. (B and A can be multiplied by the same nonzero constant to carry out this normalization when necessary.)
Figure 2.4 shows one of the intermediate plots produced by the sine-wave analysis routine in Fig.. This figure corresponds to Fig.1.6 in §1.3 (see page ). In Fig.2.4a, we see samples of the input test sinusoid overlaid with the continuous sinusoid represented by those samples. Figure 2.4b shows only the samples of the filter output signal: While we know the output signal becomes a sampled sinusoid after the one-sample transient response, we do not know its amplitude or phase until we measure it; the underlying continuous signal represented by the samples is therefore not plotted. (If we really wanted to see this, we could use software for bandlimited interpolation , such as Matlab's interp function.) A plot such as Fig.2.4 is produced for each test frequency, and the relative amplitude and phase are measured between the input and output to form one sample of the measured frequency response, as discussed in §1.3.
Next, the one-sample start-up transient is removed from the filter output signal y to form the ``cropped'' signal yss (`` steady state''). The final task is to measure the amplitude and phase of the yss. Output amplitude estimation is done in swanal by the line
[ampout,peakloc] = max(abs(yss));Note that the peak amplitude found in this way is approximate, since the true peak of the output sinusoid generally occurs between samples. We will find the output amplitude much more accurately in the next two sections. We store the index of the amplitude peak in peakloc so it can be used to estimate phase in the next step. Given the output amplitude ampout, the amplitude response of the filter at frequency f(k) is given by
gains(k) = ampout/ampin;The last step of swanal in Fig. is to estimate the phase of the cropped filter output signal yss. Since we will have better ways to accomplish this later, we use a simplistic method here based on inverting the sinusoid analytically:
phaseDiff = acos(min(1,max(-1,yss(end)/ampOut))) ... - acos(min(1,max(-1,sss(end)/ampIn))); phaseDiff = mod2pi(phaseDiff); % reduce to [-pi,pi)The mod2pi utility reduces its scalar argument to the range ,3.6 and is listed in Fig.2.5. It deserves to be emphasized that analytically inverting a test sinusoid is not how we estimate a filter phase response in practice!
function [y] = mod2pi(x) % MOD2PI - Reduce x to the range [-pi,pi) y=x; twopi = 2*pi; while y >= pi, y = y - twopi; end while y < -pi, y = y + twopi; end
In summary, the sine-wave analysis measures experimentally the gain and phase-shift of the digital filter at selected frequencies, thus measuring the frequency response of the filter at those frequencies. It is interesting to compare these experimental results with the closed-form expressions for the frequency response derived in §1.3.2. From Equations (1.6-1.7) we have
where denotes the amplitude response (filter gain versus frequency), denotes the phase response (filter phase-shift versus frequency), is frequency in Hz, and denotes the sampling rate. Both the amplitude response and phase response are real-valued functions of (real) frequency, while the frequency response is the complex function of frequency given by .
Figure 2.6 shows overlays of the measured and theoretical results. While there is good general agreement, there are noticeable errors in the measured amplitude- and phase-response curves. While we know the input sinusoidal amplitude exactly because we generated it synthetically, the filter output amplitude was crudely approximated as the largest sample magnitude observed, without interpolation to better estimate the true peak amplitude. Phase-response errors are in turn caused by output-amplitude errors.
It is important to understand the source(s) of deviation between the measured and theoretical values. Our simulated sine-wave analysis deviates from an ideal sine-wave analysis in the following ways (listed in ascending order of importance):
The need for interpolation is lessened greatly if the sampling rate is chosen to be unrelated to the test frequencies (ideally so that the number of samples in each sinusoidal period is an irrational number). Figure 2.7 shows the measured and theoretical results obtained by changing the highest test frequency fmax from to , and the number of samples in each test sinusoid tmax from to . For these parameters, at least one sample falls very close to a true peak of the output sinusoid at each test frequency.
It should also be pointed out that one never estimates signal phase in practice by inverting the closed-form functional form assumed for the signal. Instead, we should estimate the delay of each output sinusoid relative to the corresponding input sinusoid. This leads to the general topic of time delay estimation . Under the assumption that the round-off error can be modeled as ``white noise'' (typically this is an accurate assumption), the optimal time-delay estimator is obtained by finding the (interpolated) peak of the cross-correlation between the input and output signals. For further details on cross-correlation, a topic in statistical signal processing, see, e.g., [77,87].
Using the theory presented in later chapters, we will be able to compute very precisely the frequency response of any LTI digital filter without having to resort to bandlimited interpolation (for measuring amplitude response) or time-delay estimation (for measuring phase).