locaccuracy.m

基于声源的方向定位程序

% locAccuracy.m
% Given arr (a set of phone positions), calculate and plot the 
% accuracy of locations calculated with the time-delay estimation method.
%
% For example, this one looks pretty:
% arr = [0 0; 0 1; 1 1; 1 0]' - 0.5 + rand(2,4)/1e5; locAccuracy
%
% Dave Mellinger
% 11/97

n = size(arr,2);		% number of phones
lim = defaultLimits(arr);

% res = number of points that are plotted in x and y; use (prime number)+1
res = 200;			% most accurate
%res = 102;			% intermediate
%res = 60;			% most speedy

ncolor = 17;			% number of contours

x = linspace(lim(1), lim(2), res);
y = linspace(lim(3), lim(4), res);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate location accuracy on grid defined by x and y.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (1)				% change this 1 into 0 to avoid recalculation

  [h1,h2] = timesToDelays(1:n);		% all pairs of phones (O(n^2) of them)
  [j1,j2] = timesToDelays(1:length(h1));% all pairs of pairs (O(n^4) of them!)

  fprintf(2, '%d dots: ', length(x))
  d = zeros(res,res);
  for i = 1:length(x)
    for j = 1:length(y)
      jac = CalcDeltaJacobian([x(i);y(j)], arr, h1, h2, h1);
      d(j,i) = loccond(jac, j1,j2);
    end
    fprintf(1,'.');
  end
  disp(' ');
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Plotting
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clf
hold on

if (1), d1 = -d;		% linear color mapping
else d1 = -log(d);		% logarithmic color mapping
end

% Manually scale d1 to fit the number of colors.
d2 = d1 - min(min(d1));
d2 = d2 / max(max(d2));

xcorrection = diff(x(1:2))/4;	    % not clear why this is needed, but it is
image(x + xcorrection, y, round(d2 * ncolor + 0.4999));
cmap = hot(round(ncolor * 1.1));
colormap(cmap(size(cmap,1) - ncolor + 1 : size(cmap,1), :))

contour(x, y, d2, ncolor-1, 'k')

set(plot(arr(1,:), arr(2,:), 'bo'), 'LineWidth', 4)
hold off
axis(lim)
axis equal
title('Relative location accuracy')