InformationFilter

Introduction and Overview

This is a basic implementation of the information filter.


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.

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

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.
B

control transition matrix

K

Kalman gain

P_inv

inverse covariance matrix

Q

Process uncertainty

R_inv

measurement uncertainty

S

system uncertainy in measurement space

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.
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, P)
State vector and covariance array of the prediction.
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
predict(u=0)

Predict next position.

Parameters

u : np.array
Optional control vector. If non-zero, it is multiplied by B to create the control input into the system.
residual_of(z)

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

update(z, R_inv=None)

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.
y

measurement residual (innovation)