tracking_demo.m
上传用户:mozhenmi
上传日期:2008-02-18
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文件大小:1k
- % Make a point move in the 2D plane
- % State = (x y xdot ydot). We only observe (x y).
- % This code was used to generate Figure 17.9 of "Artificial Intelligence: a Modern Approach",
- % Russell and Norvig, 2nd edition, Prentice Hall, in preparation.
- % X(t+1) = F X(t) + noise(Q)
- % Y(t) = H X(t) + noise(R)
- ss = 4; % state size
- os = 2; % observation size
- F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1];
- H = [1 0 0 0; 0 1 0 0];
- Q = 0.1*eye(ss);
- R = 1*eye(os);
- initx = [10 10 1 0]';
- initV = 10*eye(ss);
- seed = 9;
- rand('state', seed);
- randn('state', seed);
- T = 15;
- [x,y] = sample_lds(F, H, Q, R, initx, T);
- [xfilt, Vfilt, VVfilt, loglik] = kalman_filter(y, F, H, Q, R, initx, initV);
- [xsmooth, Vsmooth] = kalman_smoother(y, F, H, Q, R, initx, initV);
- dfilt = x([1 2],:) - xfilt([1 2],:);
- mse_filt = sqrt(sum(sum(dfilt.^2)))
- dsmooth = x([1 2],:) - xsmooth([1 2],:);
- mse_smooth = sqrt(sum(sum(dsmooth.^2)))
- subplot(2,1,1)
- hold on
- plot(x(1,:), x(2,:), 'ks-');
- plot(y(1,:), y(2,:), 'g*');
- plot(xfilt(1,:), xfilt(2,:), 'rx:');
- for t=1:T, gaussplot(xfilt(1:2,t), Vfilt(1:2, 1:2, t), 1); end
- hold off
- legend('true', 'observed', 'filtered', 0)
- xlabel('X1')
- ylabel('X2')
- subplot(2,1,2)
- hold on
- plot(x(1,:), x(2,:), 'ks-');
- plot(y(1,:), y(2,:), 'g*');
- plot(xsmooth(1,:), xsmooth(2,:), 'rx:');
- for t=1:T, gaussplot(xsmooth(1:2,t), Vsmooth(1:2, 1:2, t), 1); end
- hold off
- legend('true', 'observed', 'smoothed', 0)
- xlabel('X1')
- ylabel('X2')