HInfinityFilter

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)

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.

Parameters:

dim_xint

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_zint

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

dim_uint

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

gammafloat

.. 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)

update(z)

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

Parameters:

zndarray

measurement for this update.

predict(u=0)

Predict next position.

Parameters:

undarray

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)

Batch processes a sequences of measurements.

Parameters:

Zslist-like

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

update_firstbool, default=False, optional,

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

saverfilterpy.common.Saver, optional

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

Returns:

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.

get_prediction(u=0)

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

Parameters:

undarray

optional control input

Returns:

xndarray

State vector of the prediction.

residual_of(z)

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

measurement_of_state(x)

Helper function that converts a state into a measurement.

Parameters:

xndarray

H-Infinity state vector

Returns:

zndarray

measurement corresponding to the given state

property V

measurement noise matrix