NOTE: Imminent drop of support of Python 2.7, 3.4. See section below for details.

This library provides Kalman filtering and various related optimal and non-optimal filtering software written in Python. It contains Kalman filters, Extended Kalman filters, Unscented Kalman filters, Kalman smoothers, Least Squares filters, fading memory filters, g-h filters, discrete Bayes, and more.

This is code I am developing in conjunction with my book Kalman and Bayesian Filter in Python, which you can read/download at

My aim is largely pedalogical - I opt for clear code that matches the equations in the relevant texts on a 1-to-1 basis, even when that has a performance cost. There are places where this tradeoff is unclear - for example, I find it somewhat clearer to write a small set of equations using linear algebra, but numpy's overhead on small matrices makes it run slower than writing each equation out by hand. Furthermore, books such Zarchan present the written out form, not the linear algebra form. It is hard for me to choose which presentation is 'clearer' - it depends on the audience. In that case I usually opt for the faster implementation.

I use NumPy and SciPy for all of the computations. I have experimented with Numba and it yields impressive speed ups with minimal costs, but I am not convinced that I want to add that requirement to my project. It is still on my list of things to figure out, however.

Sphinx generated documentation lives at Generation is triggered by git when I do a check in, so this will always be bleeding edge development version - it will often be ahead of the released version.

Plan for dropping Python 2.7 support

I haven't finalized my decision on this, but NumPy is dropping Python 2.7 support in December 2018. I will certainly drop Python 2.7 support by then; I will probably do it much sooner.

At the moment FilterPy is on version 1.x. I plan to fork the project to version 2.0, and support only Python 3.5+. The 1.x version will still be available, but I will not support it. If I add something amazing to 2.0 and someone really begs, I might backport it; more likely I would accept a pull request with the feature backported to 1.x. But to be honest I don't forsee this happening.

Why 3.5+, and not 3.4+? 3.5 introduced the matrix multiply symbol, and I want my code to take advantage of it. Plus, to be honest, I'm being selfish. I don't want to spend my life supporting this package, and moving as far into the present as possible means a few extra years before the Python version I choose becomes hopelessly dated and a liability. I recognize this makes people running the default Python in their linux distribution more painful. All I can say is I did not decide to do the Python 3 fork, and I don't have the time to support the bifurcation any longer.

I am making edits to the package now in support of my book; once those are done I'll probably create the 2.0 branch. I'm contemplating a SLAM addition to the book, and am not sure if I will do this in 3.5+ only or not.


The most general installation is just to use pip, which should come with any modern Python distribution.

pip install filterpy

If you prefer to download the source yourself

cd <directory you want to install to>
git clone
python install

If you use Anaconda, you can install from the conda-forge channel. You will need to add the conda-forge channel if you haven't already done so:

**::**conda config --add channels conda-forge

and then install with:

**::**conda install filterpy

And, if you want to install from the bleeding edge git version

pip install git+

Note: I make no guarantees that everything works if you install from here. I'm the only developer, and so I don't worry about dev/release branches and the like. Unless I fix a bug for you and tell you to get this version because I haven't made a new release yet, I strongly advise not installing from git.

Basic use

Full documentation is at

First, import the filters and helper functions.

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

Now, create the filter

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

Initialize the filter's matrices.

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:

    # do something with the output
    x = my_filter.x

Sorry, that is the extent of the documentation here. However, the library is broken up into subdirectories: gh, kalman, memory, leastsq, and so on. Each subdirectory contains python files relating to that form of filter. The functions and methods contain pretty good docstrings on use.

My book uses this library, and is the place to go if you are trying to learn about Kalman filtering and/or this library. These two are not exactly in sync - my normal development cycle is to add files here, test them, figure out how to present them pedalogically, then write the appropriate section or chapter in the book. So there is code here that is not discussed yet in the book.


This library uses NumPy, SciPy, Matplotlib, and Python.

I haven't extensively tested backwards compatibility - I use the Anaconda distribution, and so I am on Python 3.6 and 2.7.14, along with whatever version of NumPy, SciPy, and matplotlib they provide. But I am using pretty basic Python - numpy.array, maybe a list comprehension in my tests.

I import from future to ensure the code works in Python 2 and 3.


All tests are written to work with py.test. Just type py.test at the command line.

As explained above, the tests are not robust. I'm still at the stage where visual plots are the best way to see how things are working. Apologies, but I think it is a sound choice for development. It is easy for a filter to perform within theoretical limits (which we can write a non-visual test for) yet be 'off' in some way. The code itself contains tests in the form of asserts and properties that ensure that arrays are of the proper dimension, etc.


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...".