HInfinityFilter¶
Introduction and Overview¶
Copyright 2014 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.
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class
filterpy.hinfinity.
HInfinityFilter
(dim_x, dim_z, dim_u, gamma)[source]¶ -
__init__
(dim_x, dim_z, dim_u, gamma)¶ Create an 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_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.
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P
¶ covariance matrix property
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batch_filter
(Zs, Rs=None, update_first=False)¶ Batch processes a sequences of measurements.
Parameters
- Zs : list-like
- list of measurements at each time step self.dt Missing measurements must be represented by ‘None’.
- Rs : list-like, optional
- optional list of values to use for the measurement error covariance; a value of None in any position will cause the filter to use self.R for that time step.
- update_first : bool, optional,
- controls whether the order of operations is update followed by predict, or predict followed by update. Default is predict->update.
Returns
- means: np.array((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: np.array((n,dim_x,dim_x))
- array of the covariances for each time step. In other words covariance[k,:,:] is the covariance at step k.
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get_prediction
(u=0)¶ Predicts the next state of the filter and returns it. Does not alter the state of the filter.
Parameters
- u : np.array
- optional control input
Returns
- x : numpy.ndarray
- State vecto of the prediction.
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measurement_of_state
(x)¶ Helper function that converts a state into a measurement.
Parameters
- x : np.array
- kalman state vector
Returns
- z : np.array
- measurement corresponding to the given state
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predict
(u=0)¶ Predict next position.
Parameters
- u : np.array
- Optional control vector. If non-zero, it is multiplied by G to create the control input into the system.
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residual_of
(z)¶ returns the residual for the given measurement (z). Does not alter the state of the filter.
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update
(Z)¶ Add a new measurement (Z) to the kalman filter. If Z is None, nothing is changed.
Parameters
- Z : np.array
- measurement for this update.
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x
¶ state vector property
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