# InformationFilter¶

## Introduction and Overview¶

This is a basic implementation of the information filter.

Copyright 2015 Roger R Labbe Jr.

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

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

class filterpy.kalman.InformationFilter(dim_x, dim_z, dim_u=0)[source]

Create a linear Information filter. Information filters compute the inverse of the Kalman filter, allowing you to easily denote having no information at initialization.

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 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 (optional) size of the control input, if it is being used. Default value of 0 indicates it is not used.

Examples

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

Attributes: x : numpy.array(dim_x, 1) State estimate vector P_inv : numpy.array(dim_x, dim_x) inverse state covariance matrix x_prior : numpy.array(dim_x, 1) Prior (predicted) state estimate. The *_prior and *_post attributes are for convienence; they store the prior and posterior of the current epoch. Read Only. P_inv_prior : numpy.array(dim_x, dim_x) Inverse prior (predicted) state covariance matrix. Read Only. x_post : numpy.array(dim_x, 1) Posterior (updated) state estimate. Read Only. P_inv_post : numpy.array(dim_x, dim_x) Inverse posterior (updated) state covariance matrix. Read Only. z : ndarray Last measurement used in update(). Read only. R_inv : numpy.array(dim_z, dim_z) inverse of measurement noise matrix Q : numpy.array(dim_x, dim_x) Process noise matrix H : numpy.array(dim_z, dim_x) Measurement function y : numpy.array Residual of the update step. Read only. K : numpy.array(dim_x, dim_z) Kalman gain of the update step. Read only. S : numpy.array Systen uncertaintly projected to measurement space. Read only. log_likelihood : float log-likelihood of the last measurement. likelihood : float Computed from the log-likelihood. inv : function, default numpy.linalg.inv If you prefer another inverse function, such as the Moore-Penrose pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv
__init__(dim_x, dim_z, dim_u=0)[source]

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

update(z, R_inv=None)[source]

Add a new measurement (z) to the kalman filter. If z is None, nothing is changed.

Parameters: z : np.array measurement for this update. R : np.array, scalar, or None Optionally provide R to override the measurement noise for this one call, otherwise self.R will be used.
predict(u=0)[source]

Predict next position.

Parameters: u : ndarray Optional control vector. If non-zero, it is multiplied by B to create the control input into the system.
batch_filter(zs, Rs=None, update_first=False, saver=None)[source]

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

log-likelihood of the last measurement.

likelihood

Computed from the log-likelihood. The log-likelihood can be very small, meaning a large negative value such as -28000. Taking the exp() of that results in 0.0, which can break typical algorithms which multiply by this value, so by default we always return a number >= sys.float_info.min.

F

State Transition matrix