Filterpy_zh_cn

FilterPy - Python

****Python 2.7,3.4

Python g-h

Python / https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/

• • numpyZarchan

NumPySciPyNumba

Sphinxhttp://filterpy.readthedocs.org/ git -

Python 2.7------------------------------------

NumPy201812Python 2.7Python 2.7;

FilterPy1.x. 2.0Python 3.5+1.x2.0;1.x.

3.5+3.4+3.5PythonLinuxPythonPython 3

;2.0 SLAM3.5+

pipPython

pip install filterpy

cd <directory you want to install to>
git clone http://github.com/rlabbe/filterpy
python setup.py install

Anacondaconda-forgeconda-forge

::

conda config --add channels conda-forge

::

conda install filterpy

git

pip install git+https://github.com/rlabbe/filterpy.git

/buggit

https://filterpy.readthedocs.io/en/latest/

import numpy as np
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise

my_filter = KalmanFilter(dim_x=2, dim_z=1)

my_filter.x = np.array([[2.],
                [0.]])       # initial state (location and velocity)

my_filter.F = np.array([[1.,1.],
                [0.,1.]])    # state transition matrix

my_filter.H = np.array([[1.,0.]])    # Measurement function
my_filter.P *= 1000.                 # covariance matrix
my_filter.R = 5                      # state uncertainty
my_filter.Q = Q_discrete_white_noise(2, dt, .1) # process uncertainty

Finally, run the filter.

while True:
    my_filter.predict()
    my_filter.update(get_some_measurement())

    # do something with the output
    x = my_filter.x
    do_something_amazing(x)

ghkalmanmemoryleastsq python

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python// -

NumPySciPyMatplotlibPython

• AnacondaPython 3.62.7.14NumPySciPymatplotlibPython - numpy.array

** __ future __ **Python 23

py.test``py.test``

I use three main texts as my refererence, though I do own the majority of the Kalman filtering literature. First is Paul Zarchan's 'Fundamentals of Kalman Filtering: A Practical Approach'. I think it by far the best Kalman filtering book out there if you are interested in practical applications more than writing a thesis. The second book I use is Eli Brookner's 'Tracking and Kalman Filtering Made Easy'. This is an astonishingly good book; its first chapter is actually readable by the layperson! Brookner starts from the g-h filter, and shows how all other filters - the Kalman filter, least squares, fading memory, etc., all derive from the g-h filter. It greatly simplifies many aspects of analysis and/or intuitive understanding of your problem. In contrast, Zarchan starts from least squares, and then moves on to Kalman filtering. I find that he downplays the predict-update aspect of the algorithms, but he has a wealth of worked examples and comparisons between different methods. I think both viewpoints are needed, and so I can't imagine discarding one book. Brookner also focuses on issues that are ignored in other books - track initialization, detecting and discarding noise, tracking multiple objects, an so on.

I said three books. I also like and use Bar-Shalom's Estimation with Applications to Tracking and Navigation. Much more mathematical than the previous two books, I would not recommend it as a first text unless you already have a background in control theory or optimal estimation. Once you have that experience, this book is a gem. Every sentence is crystal clear, his language is precise, but each abstract mathematical statement is followed with something like "and this means...".

License

The MIT License (MIT)

Copyright (c) 2015 Roger R. Labbe Jr

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.TION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.