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Thomas

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22 Feb 2013	Imagine 4D/3D image visualization and evaluation GUI. Author: Christian Wuerslin	Best MRI image visualization tool!
22 Feb 2013	Imagine 4D/3D image visualization and evaluation GUI. Author: Christian Wuerslin

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24 Apr 2013		Imagine 4D/3D image visualization and evaluation GUI. Author: Christian Wuerslin	3d, evaluation, image analysis, 4d, image, 3d image	475	9	5.0 5.0 \| 4 ratings

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File	Learning the Kalman Filter

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12 Oct 2005		Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder	basic kalman filter, kalman filter, discrete filter, linear, imtransform, filter analysis	482	98	4.44944 4.4 \| 91 ratings

Comments and Ratings Matching Thomas' Watch List			1-5 of 134
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10 Apr 2013	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	jakub	In other words, how to draw their values, provided they are stored in my data.mat? Will the "plot" are the same and I have to use a loop "for"? Ask directly about the code sample, it's here I deal with a couple of hours.	Comment only
09 Apr 2013	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	jakub	My case: >> clear s >> s.x = 12; >> s.A = 1; >> s.Q = 2^2; >> s.H = 1; >> s.R = 2^2; >> s.B = 0; >> s.u = 0; >> s.x = nan; >> s.P = nan; >> s.z = [1 2 3 4 5 6 7 8 9 22 3 4 5 6 7 8 88]; >> kalmanf(s); >> figure >> hold on >> grid on >> hz=plot(s.z,'r.') >> hk=plot(s,'b-') Error using plot Conversion to double from struct is not possible. Where is the problem ? Thank u	Comment only
13 Feb 2013	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	zhang blue		5
07 Feb 2013	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Tim		5
07 Feb 2013	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Tim	I am new to k. filtering, and I don't understand the following: the covariance matrix P appears to be only a function of the following inputs: A (defined by the system), P (itself), Q (known p.noise cov.), and H (typically identity), and also the Kalman gain K. K itself is a function only of P, H, and R (known m.noise cov.). None of these are re-defined during iteration except P. How then is the estimate of the covariance matrix P tied to observations z? The result of this appears to be that the Kalman gain explodes by the third or fourth iteration, making the output mirror the noisy input. This seems to be what Enver Bahar is noticing too, I think.	3