| exp | ||
| styles | ||
| bar_plot.py | ||
| book_format.py | ||
| DiscreteBayes1D.py | ||
| dog_track_1d.py | ||
| DogSensor.py | ||
| g-h_filter.ipynb | ||
| gauss.py | ||
| gaussian_internal.py | ||
| Gaussians.ipynb | ||
| gh.py | ||
| histogram_filter.ipynb | ||
| histogram.py | ||
| Introduction.ipynb | ||
| Kalman_Filters.ipynb | ||
| KalmanFilter1D.py | ||
| KalmanFilter.py | ||
| mkf_ellipse_test.py | ||
| mkf_internal.py | ||
| Multidimensional_Kalman_Filters.ipynb | ||
| noise.py | ||
| README.md | ||
| README.md~ | ||
| stats.py | ||
| test.py | ||
| Untitled0.ipynb | ||
#Kalman Filters and Random Signals in Python
Version 0.0 - not ready for public consumption. In development.
this is a book BLAH BLAH BLAH
Contents
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Introduction to the Kalman filter. Explanation of the idea behind this book.
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Chapter 1: The g-h Filter Intuitive introduction to the g-h filter, which is a family of filters that includes the Kalman filter. Not filler - once you understand this chapter you will understand the concepts behind the Kalman filter.
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Chapter 2: The Discrete Bayes Filter Introduces the Discrete Bayes Filter. From this you will learn the probabilistic reasoning that underpins the Kalman filter in an easy to digest form.
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Chapter 3: Gaussian Probabilities Introduces using Gaussians to represent beliefs. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.
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Chapter 4: One Dimensional Kalman Filters Implements a Kalman filter by modifying the Discrete Bayesian Filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems.
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Chapter 5: Multidimensional Kalman Filter We extend the Kalman filter developed in the previous chapter to the full, generalized filter.