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Added Adaptive Filtering chapter.

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Still a lot of work to be done on it. This is just an
initial commit.
This commit is contained in:
Roger Labbe 2015-01-11 00:13:12 -08:00
parent 573c9429c8
commit 026413dbd4
5 changed files with 1110 additions and 6 deletions
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2
09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb
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@ -1054,7 +1054,7 @@
"\n",
" for i in range(int(20/dt)):\n",
" z = radar.get_range()\n",
" rk.update(array([[z]]), HJacobian_at, hx)\n",
" rk.update(array([z]), HJacobian_at, hx)\n",
" rk.predict()\n",
" \n",
"Putting that all together along with some boilerplate code to save the results and plot them, we get"

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15_Adaptive_Filtering/Adaptive_Filtering.ipynb Normal file
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4
README.md
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@ -160,6 +160,10 @@ Kalman filters are recursive, and thus very suitable for real time filtering. Ho
* [**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering/Adaptive_Filtering.ipynb)
Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.
* [**Chapter XX: Numerical Stability**](not implemented)
Not written yet.

2
report.tplx
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@ -18,5 +18,5 @@
%((* endblock predoc *))
((* block docclass *))
\documentclass[20pt]{book}
\documentclass[4pt]{book}
((* endblock docclass *))

11
table_of_contents.ipynb
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@ -92,8 +92,14 @@
"\n",
"\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",
"\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",
"\n",
"[**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering/Adaptive_Filtering.ipynb)\n",
" \n",
"Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
"\n",
" \n",
"[**Chapter XX: Numerical Stability**](not implemented)\n",
"\n",
@ -108,9 +114,6 @@
"*This is not implemented, and I have not decided if I want to make it part of this book or not.*\n",
" \n",
"\n",
"[**Chapter XX: Multihypothesis Tracking**](not implemented)\n",
" \n",
"*Not implemented yet.*\n",
"\n",
"\n",
"[**Appendix: Installation, Python, NumPy, and filterpy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation/Appendix_Installation.ipynb)\n",