Re: [Bayes++] Bayes++ EKF Example Request

[Nicola B. <nb...@es...>]

Nithya,

Look at the documentation and the library's sources for the classes 
"Covariance_scheme", "Linrz_predict_model" and 
"Linrz_(un)correlated_observe_model" for the EKF implementation.

Also, if you change "Unscented_scheme" with "Covariance_scheme" in the file 
"simpleExample.cpp", you have an example of classic Kalman Filter.

Finally, if you didn't do it yet, take a look at this good introduction:
http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html

Hope it helps.

Nicola



On Tuesday 25 Jul 2006 16:37, Nithya Nirmal Vijayakumar wrote:
> Hi,
>
> I am working on a prediction system implemented using Extended Kalman
> filters. Ideally, my system would take a set of time series data points
> and fill in any missing values. I have installed Bayes++ and tried out the
> examples. I am looking for more examples on extended kalman filters
> implemented using Bayes++. As a novice to Kalman filters, I greatly
> appreciate any help.
>
> To begin with, I am working on extending the position velocity example to
> take in a set of observations made at 10 time points, update the kalman
> filter and then predict the observations for the 11th through 15th time
> points. I would like to use EKF. I wrote a simple example using the kalman
> filter toolbox in matlab. I appreciate any help in coding this up in
> Bayes++. The partial code in matlab is as follows.
>
> ------position_velocity.m----------
>
> %Take as observation vector and velocity vector values
>
> %set F, H, Q, R values
> ss = 2; % state size
> os = 1; % observation size
> F = [1 dt; ...
>          0 1];
> H = [1 0];
> Q = 0.1*eye(ss);
> R = 1*eye(os);
>
> initx = [obsVec(1); velVec(1)]
> initV = 10*eye(ss)
>
> %first 10 observations. perform kalman update
> T = 10;
> xfilt(:,1) = initx;
> Vfilt(:,:,1) = initV;
> for i = 2:T
>      [xfilt(:,i), Vfilt(:,:,i)] = kalman_update(F, H, Q, R, obsVec(i), ...
>      xfilt(:,i-1), Vfilt(:,:,i-1), 'initial', 0);
> end
>
> %predict next 5 values
> x = [obsVec; velVec];
> temp = zeros(2,2);
> for i=T:T+5
>      %calculate next position
>      temp = F*xfilt(:,i-1);
>      %update filter
>      [xfilt(:,i), Vfilt(:,:,i)] = kalman_update(F, H, Q, R, temp(1), ...
>      xfilt(:,i-1),Vfilt(:,:,i-1), 'initial', 0);
> end
>
>
> thanks,
> Nithya
>
>
>
>
>
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-- 
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Nicola Bellotto
University of Essex
Department of Computer Science
Wivenhoe Park
Colchester CO4 3SQ
United Kingdom

Room: 1N1.2.8
Tel. +44 (0)1206 874094
URL: http://privatewww.essex.ac.uk/~nbello
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