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

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

Create a Information 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 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.

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)[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.

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.

get_prediction(u=0)[source]

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, P)

State vector and covariance array of the prediction.

residual_of(z)[source]

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

measurement_of_state(x)[source]

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

Q

Process uncertainty

P_inv

inverse covariance matrix

R_inv

inverse measurement uncertainty

H

Measurement function

F

State Transition matrix

B

control transition matrix

x

State estimate vector

K

Kalman gain

y

measurement residual (innovation)

S

system uncertainy in measurement space