KalmanFilter

This page describes a module in the upcoming version 1.2 of Ikaros and may not be correct for version 1.1

The module implements a standard Kalman filter. The process is described by the equation (+1) = ()+(), where is the state of the process and is the control input. The observation (or measurement) is described by (). The process variance is set by the parameter and the measurement variance by .

Example XML definition

A simple example

  <module
      class = "KalmanFilter"
      name = "KalmanFilter"
      observation_size = "1"
      state_size = "1"
      A = ""
      B = ""
      H = ""
  />

Parameters

Name	Description	Type	Default
class	The class name for the module; must be "KalmanFilter".	string
name	The name of this instance of the module.	string
A	The state transition matrix (state gain) [n x n]	matrix
B	The input gain [n x m]	matrix
H	The output gain [m x n]	matrix
state_size	The size of the state	int	1
observation_size	The size of the observation (measurement noise)	int	1
process_noise	Noise for the process	float	1
observation_noise	Noise for each observation	float	1

Module Connections

Inputs

Name	Description
INPUT	The input [1 x m]
OBSERVATION	The observation [1 x m]

Outputs

Name	Description
STATE	The state [n x 1]
INNOVATION	The state [m x 1]
KALMAN_GAIN	The Kalman gain [n x m]

Limitations

It should be possible to specify the full covariance matrices for the process and measurements (R and Q).

The matrices A, B and H should also be able to use inputs instead of parameters.

Observation and state sizes should be inferred from the matrix parameters.

Author

Christian Balkenius
christian.balkenius@lucs.lu.se
Lund Univeristy Cognitive Science

Files

KalmanFilter.h
KalmanFilter.cc
KalmanFilter.ikc