daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Regenerated PDF.

Browse Source 
Needed to fix a latex bug in the ensemble chapter for that to work,
and to add the changes to the chapter numbers in the merge script.
This commit is contained in:
Roger Labbe 2014-12-08 22:48:00 -08:00
parent 3f798c6717
commit 6721ae4dc3
2 changed files with 4 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb
View File

@ -325,7 +325,7 @@
"source": [
"## The Algorithm\n",
"\n",
"As I already stated, when the filter is initialized a large number of sigma points are drawn from the initial state ($\\mathbf{x}) and covariance ($\\mathbf{P}$. From there the algorithm proceeds very similarly to the UKF. During the prediction step the sigma points are passed through the state transition function, and then perturbed by adding a bit of noise to account for the process noise. During the update step the sigma points are translated into measurement space by passing them through the measurement function, they are perturbed by a small amount to account for the measurement noise. The Kalman gain is computed from the \n",
"As I already stated, when the filter is initialized a large number of sigma points are drawn from the initial state ($\\mathbf{x}$) and covariance ($\\mathbf{P}$). From there the algorithm proceeds very similarly to the UKF. During the prediction step the sigma points are passed through the state transition function, and then perturbed by adding a bit of noise to account for the process noise. During the update step the sigma points are translated into measurement space by passing them through the measurement function, they are perturbed by a small amount to account for the measurement noise. The Kalman gain is computed from the \n",
"\n",
"We already mentioned the main difference between the UKF and EnKF - the UKF choses the sigma points deterministically. There is another difference, implied by the algorithm above. With the UKF we generate new sigma points during each predict step, and after passing the points through the nonlinear function we reconstitute them into a mean and covariance by using the *unscented transform*. The EnKF just keeps propagating the originally created sigma points; we only need to compute a mean and covariance as outputs for the filter! \n",
"\n",

8
merge_book.py
View File

@ -53,11 +53,9 @@ if __name__ == '__main__':
'08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb',
'09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb',
'10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb',
'11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
'12_HInfinity_Filters/HInfinity_Filters.ipynb',
'11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb',
'12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb',
'13_HInfinity_Filters/HInfinity_Filters.ipynb',
'14_Smoothing/Smoothing.ipynb',
'Appendix_A_Installation/Appendix_Installation.ipynb',
'Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb'])
# merge_notebooks(['Preface.ipynb', g-h_filter.ipynb discrete_bayes.ipynb Gaussians.ipynb Kalman_Filters.ipynb Multivariate_Kalman_Filters.ipynb Kalman_Filter_Math.ipynb Designing_Kalman_Filters.ipynb Extended_Kalman_Filters.ipynb Unscented_Kalman_Filter.ipynb'])