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Added links and description of progress.

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A few chapters were missing links, and many lacked accurate
text describing their current status.
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Roger Labbe 2014-09-18 15:22:00 -07:00
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table_of_contents.ipynb
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@ -20,7 +20,7 @@
"\n",
"[**Preface**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Preface.ipynb)\n",
" \n",
"Explanation of the idea behind this book and how to use it.\n",
"Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.\n",
"\n",
"\n",
"[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter01_gh_filter/g-h_filter.ipynb)\n",
@ -35,12 +35,12 @@
"\n",
"[**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter03_Least_Squares/Least_Squares_Filters.ipynb)\n",
"\n",
"Introduces the least squares filter in batch and recursive forms.\n",
"Introduces the least squares filter in batch and recursive forms. I've not made a start on authoring this yet.\n",
"\n",
"\n",
"[**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter04_Gaussians/Gaussians.ipynb)\n",
"\n",
"Introduces using Gaussians to represent beliefs. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.\n",
"Introduces using Gaussians to represent beliefs in the Bayesian sense. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.\n",
"\n",
"\n",
"[**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter05_Kalman_Filters/Kalman_Filters.ipynb)\n",
@ -55,7 +55,9 @@
"\n",
"[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb)\n",
"\n",
"We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know this.\n",
"We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know this. \n",
"\n",
"*This still needs a lot of work. *\n",
"\n",
"\n",
"[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb)\n",
@ -67,40 +69,50 @@
"\n",
"Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.\n",
"\n",
"*Still very early going on this chapter.*\n",
"\n",
"[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb)\n",
"\n",
"\n",
"Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
"\n",
"*Still early going.*\n",
"\n",
"\n",
"\n",
"[**Chapter 11: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)\n",
"\n",
"Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n",
"\n",
"[**Chapter 12: H-Infinity Filters**](not implemented)\n",
"\n",
"[**Chapter 12: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter12_HInfinity_Filters/HInfinity_Filters.ipynb)\n",
" \n",
"EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n",
"Describes the $H_\\infty$ filter. \n",
"\n",
"\n",
"EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n",
"*I have code that implements the filter, but no supporting text yet.*\n",
"\n",
"\n",
"[**Chapter 13: Numerical Stability**](not implemented)\n",
"\n",
"EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n",
"\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**](not implemented)\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",
"*Not implemented. The filterpy library does contain some smothers, however.*\n",
" \n",
"[**Chapter 15: Particle Filters**](not implemented)\n",
" \n",
"Particle filters uses a Monte Carlo technique to \n",
"Particle filters uses a Monte Carlo technique to filter. \n",
"\n",
"*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 16: Multihypothesis Tracking**](not implemented)\n",
" \n",
"description\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",
@ -113,6 +125,8 @@
"\n",
"Symbols and notations used in this book. Comparison with notations used in the literature.\n",
"\n",
"*Still just a collection of notes at this point.*\n",
"\n",
"\n",
"### Github repository\n",
"http://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n"