From 60e31b890d2e5b9869ec2de82ede36163b080c1e Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Sun, 16 Nov 2014 16:12:14 -0800 Subject: [PATCH] Add references to smoothing chapter. --- 14_Smoothing/{14_Smoothing.ipynb => Smoothing.ipynb} | 0 README.md | 2 +- merge_book.py | 2 ++ table_of_contents.ipynb | 2 +- 4 files changed, 4 insertions(+), 2 deletions(-) rename 14_Smoothing/{14_Smoothing.ipynb => Smoothing.ipynb} (100%) diff --git a/14_Smoothing/14_Smoothing.ipynb b/14_Smoothing/Smoothing.ipynb similarity index 100% rename from 14_Smoothing/14_Smoothing.ipynb rename to 14_Smoothing/Smoothing.ipynb diff --git a/README.md b/README.md index 2ec117b..d70053b 100644 --- a/README.md +++ b/README.md @@ -146,7 +146,7 @@ EKF and UKF are linear approximations of nonlinear problems. Unless programmed c * [**Chapter 13: Numerical Stability**](not implemented) -* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb) +* [**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb) Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches. diff --git a/merge_book.py b/merge_book.py index 5300119..45de288 100644 --- a/merge_book.py +++ b/merge_book.py @@ -54,6 +54,8 @@ if __name__ == '__main__': '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', + '14_Smoothing/Smoothing.ipynb', 'Appendix_A_Installation/Appendix_Installation.ipynb', 'Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb']) diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb index dbde96f..d249c07 100644 --- a/table_of_contents.ipynb +++ b/table_of_contents.ipynb @@ -97,7 +97,7 @@ "\n", "*This chapter is not started. I'm likely to rearrange where this material goes - this is just a placeholder.*\n", "\n", - "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/14_Smoothing.ipynb)\n", + "[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing/Smoothing.ipynb)\n", " \n", "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n", " \n",