Copyright 2015 Roger R Labbe Jr.

FilterPy library. http://github.com/rlabbe/filterpy

Documentation at: https://filterpy.readthedocs.org

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

This is licensed under an MIT license. See the readme.MD file for more information.

class filterpy.hinfinity.HInfinityFilter(dim_x, dim_z, dim_u, gamma)[source]

H-Infinity filter. You are responsible for setting the various state variables to reasonable values; the defaults below will not give you a functional filter.

dim_x : int

Number of state variables for the Kalman filter. For example, if you are tracking the position and velocity of an object in two dimensions, dim_x would be 4.

This is used to set the default size of P, Q, and u

dim_z : int

Number of of measurement inputs. For example, if the sensor provides you with position in (x, y), dim_z would be 2.

dim_u : int

Number of control inputs for the Gu part of the prediction step.

gamma : float
.. warning::

I do not believe this code is correct. DO NOT USE THIS. In particular, note that predict does not update the covariance matrix.

__init__(dim_x, dim_z, dim_u, gamma)[source]

x.__init__(…) initializes x; see help(type(x)) for signature


Add a new measurement z to the H-Infinity filter. If z is None, nothing is changed.

z : ndarray

measurement for this update.


Predict next position.

u : ndarray

Optional control vector. If non-zero, it is multiplied by B to create the control input into the system.

batch_filter(Zs, update_first=False, saver=False)[source]

Batch processes a sequences of measurements.

Zs : list-like

list of measurements at each time step self.dt Missing measurements must be represented by ‘None’.

update_first : bool, default=False, optional,

controls whether the order of operations is update followed by predict, or predict followed by update.

saver : filterpy.common.Saver, optional

filterpy.common.Saver object. If provided, saver.save() will be called after every epoch

means: ndarray ((n, dim_x, 1))

array of the state for each time step. Each entry is an np.array. In other words means[k,:] is the state at step k.

covariance: ndarray((n, dim_x, dim_x))

array of the covariances for each time step. In other words covariance[k, :, :] is the covariance at step k.


Predicts the next state of the filter and returns it. Does not alter the state of the filter.

u : ndarray

optional control input

x : ndarray

State vector of the prediction.


returns the residual for the given measurement (z). Does not alter the state of the filter.


Helper function that converts a state into a measurement.

x : ndarray

H-Infinity state vector

z : ndarray

measurement corresponding to the given state


measurement noise matrix