signal processing using fgoalattain -凯发k8网页登录

signal processing using `fgoalattain`

consider designing a linear-phase finite impulse response (fir) filter. the problem is to design a lowpass filter with magnitude one at all frequencies between 0 and 0.1 hz and magnitude zero between 0.15 and 0.5 hz.

the frequency response h(f) for such a filter is defined by

\begin{matrix} h (f) = \sum_{n = 0}^{2 m} h (n) e^{- j 2 π f n} \\ = a (f) e^{- j 2 π f m}, \\ a (f) = \sum_{n = 0}^{m - 1} a (n) \cos (2 π f n), \end{matrix}

(1)

where a(f) is the magnitude of the frequency response. one solution is to apply a goal attainment method to the magnitude of the frequency response. given a function that computes the magnitude, will attempt to vary the magnitude coefficients a(n) until the magnitude response matches the desired response within some tolerance. the function that computes the magnitude response is given in filtmin.m. this function uses a, the magnitude function coefficients, and w, the discretization of the frequency domain of interest.

to set up a goal attainment problem, you must specify the goal and weights for the problem. for frequencies between 0 and 0.1, the goal is one. for frequencies between 0.15 and 0.5, the goal is zero. frequencies between 0.1 and 0.15 are not specified, so no goals or weights are needed in this range.

this information is stored in the variable goal passed to . the length of goal is the same as the length returned by the function filtmin. so that the goals are equally satisfied, usually weight would be set to abs(goal). however, since some of the goals are zero, the effect of using weight=abs(goal) will force the objectives with weight 0 to be satisfied as hard constraints, and the objectives with weight 1 possibly to be underattained (see goal attainment method). because all the goals are close in magnitude, using a weight of unity for all goals will give them equal priority. (using abs(goal) for the weights is more important when the magnitude of goal differs more significantly.) also, setting

options = optimoptions('fgoalattain','equalitygoalcount',length(goal));

specifies that each objective should be as near as possible to its goal value (neither greater nor less than).

step 1: write a file filtmin.m

function y = filtmin(a,w)
n = length(a);
y = cos(w'*(0:n-1)*2*pi)*a ;

step 2: invoke optimization routine

% plot with initial coefficients
a0 = ones(15,1);
incr = 50;
w = linspace(0,0.5,incr);
y0 = filtmin(a0,w);
clf, plot(w,y0,'-.b');
drawnow;
% set up the goal attainment problem
w1 = linspace(0,0.1,incr) ;
w2 = linspace(0.15,0.5,incr);
w0 = [w1 w2];
goal = [1.0*ones(1,length(w1)) zeros(1,length(w2))];
weight = ones(size(goal)); 
% call fgoalattain
options = optimoptions('fgoalattain','equalitygoalcount',length(goal));
[a,fval,attainfactor,exitflag]=fgoalattain(@(x)filtmin(x,w0),...
    a0,goal,weight,[],[],[],[],[],[],[],options);
% plot with the optimized (final) coefficients
y = filtmin(a,w);
hold on, plot(w,y,'r')
axis([0 0.5 -3 3])
xlabel('frequency (hz)')
ylabel('magnitude response (db)')
legend('initial', 'final')
grid on

compare the magnitude response computed with the initial coefficients and the final coefficients (magnitude response with initial and final magnitude coefficients). note that you could use the (signal processing toolbox) function in signal processing toolbox™ software to design this filter.

magnitude response with initial and final magnitude coefficients

the initial response looks like a damped sinusoid as the frequency (x-axis) increases. the final response is close to a piecewise linear function with value 1 at frequencies 0 to 0.1, linear from 1 to 0 as frequency increases from 0.1 to 0.15, and remains near zero for frequencies above 0.15.

signal processing using fgoalattain -凯发k8网页登录