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ukf::parameter::ukf_param Struct Reference

Structure holding the parameters of the Unscented Kalman Filter. More...

#include <ukf_types.h>

Public Attributes

double kpa
 $\kappa \geq 0$, $\kappa = 0$ is a good choice. According to van der Merwe, its value is not critical More...
 
double alpha
 $0 \leq \alpha \leq 1$ : "Size" of sigma-point distribution. Should be small if the function is strongly non-linear More...
 
double beta
 Non negative weights used to introduce knowledge about the higher order moments of the distribution. For gaussian distributions, $\beta = 2$ is a good choice. More...
 
double lambda
 $\lambda = \alpha^2 (n + \kappa) - n$ More...
 
double gamma
 $\gamma = \sqrt{\lambda + n}$ More...
 
EvolutionNoise * evolution_noise
 Parameter used for the evolution noise. More...
 
double observation_noise
 Covariance of the observation noise. More...
 
double prior_pi
 Prior estimate of the covariance matrix. More...
 
int n
 Number of parameters to estimate. More...
 
int nbSamples
 $nbSamples = (2 n + 1)$ Number of sigma-points More...
 
int no
 Dimension of the output. More...
 

Detailed Description

Structure holding the parameters of the Unscented Kalman Filter.

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.

Member Data Documentation

double ukf::parameter::ukf_param::alpha

$0 \leq \alpha \leq 1$ : "Size" of sigma-point distribution. Should be small if the function is strongly non-linear

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
double ukf::parameter::ukf_param::beta

Non negative weights used to introduce knowledge about the higher order moments of the distribution. For gaussian distributions, $\beta = 2$ is a good choice.

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
EvolutionNoise* ukf::parameter::ukf_param::evolution_noise

Parameter used for the evolution noise.

Initial value of the evolution noise Evolution noise type

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
double ukf::parameter::ukf_param::gamma

$\gamma = \sqrt{\lambda + n}$

double ukf::parameter::ukf_param::kpa

$\kappa \geq 0$, $\kappa = 0$ is a good choice. According to van der Merwe, its value is not critical

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
double ukf::parameter::ukf_param::lambda

$\lambda = \alpha^2 (n + \kappa) - n$

int ukf::parameter::ukf_param::n

Number of parameters to estimate.

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
int ukf::parameter::ukf_param::nbSamples

$nbSamples = (2 n + 1)$ Number of sigma-points

int ukf::parameter::ukf_param::no

Dimension of the output.

Examples:
example-004.cc, and example-005.cc.
double ukf::parameter::ukf_param::observation_noise

Covariance of the observation noise.

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
double ukf::parameter::ukf_param::prior_pi

Prior estimate of the covariance matrix.

Examples:
example-001.cc, example-002.cc, example-003.cc, example-004.cc, example-005.cc, and example-006.cc.
The documentation for this struct was generated from the following file:
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