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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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.