indii::ml::filter::KalmanSmootherModel

KalmanSmootherModel Class Template Reference

Inheritance diagram for KalmanSmootherModel:

Inheritance graph
[legend]

List of all members.

Detailed Description

template<class T = unsigned int>
class indii::ml::filter::KalmanSmootherModel< T >

KalmanSmoother compatible model.

Author:
Lawrence Murray <lawrence@indii.org>
Version:
Rev
Date:
Date
Parameters:
T The type of time.
See also:
indii::ml::filter for general usage guidelines.

Definition at line 22 of file KalmanSmootherModel.hpp.


Public Member Functions

virtual ~KalmanSmootherModel ()=0
 Destructor.
virtual
indii::ml::aux::GaussianPdf 		p_xtnm1_ytn (const indii::ml::aux::GaussianPdf &p_xtn_ytn, const T delta)=0
 Predict previous system state.
virtual
indii::ml::aux::GaussianPdf 		p_xtnm1_ytnm1 (const indii::ml::aux::GaussianPdf &p_xtnm1_ytn, const indii::ml::aux::vector &ytnm1, const T delta)=0
 Refine prediction of previous system state using previous measurement.

Constructor & Destructor Documentation

~KalmanSmootherModel (  )  [inline, pure virtual]

Destructor.

Definition at line 76 of file KalmanSmootherModel.hpp.

Member Function Documentation

virtual indii::ml::aux::GaussianPdf p_xtnm1_ytn ( const indii::ml::aux::GaussianPdf &  p_xtn_ytn,
const T  delta 
) [pure virtual]

Predict previous system state.

Parameters:
p_xtn_ytn $P\big(\mathbf{x}(t_n)\, | \,\mathbf{y}(t_n),\ldots,\mathbf{y}(t_T)\big)$; distribution over states at the current time given present and future measurements.
delta $\Delta t$; time step.
Returns:
$P\big(\mathbf{x}(t_n - \Delta t)\, | \,\mathbf{y}(t_n),\ldots,\mathbf{y}(t_T)\big)$; predicted distribution over states at time $t_n - \Delta t$ given future measurements.

Implemented in LinearModel.

virtual indii::ml::aux::GaussianPdf p_xtnm1_ytnm1 ( const indii::ml::aux::GaussianPdf &  p_xtnm1_ytn,
const indii::ml::aux::vector &  ytnm1,
const T  delta 
) [pure virtual]

Refine prediction of previous system state using previous measurement.

Parameters:
p_xtnm1_ytn $P\big(\mathbf{x}(t_n - \Delta t)\,|\,\mathbf{y}(t_n),\ldots,\mathbf{y}(t_T)\big)$; predicted distribution over states at time $t_n - \Delta t$ given the history of measurements. Typically obtained from prior call to p_xtnm1_ytn.
ytnm1 $\mathbf{y}(t_n - \Delta t)$; the measurement at time $t_n - \Delta t$.
delta $\Delta t$; time step.
Returns:
$P\big(\mathbf{x}(t_n - \Delta t)\, | \,\mathbf{y}(t_n - \Delta),\ldots,\mathbf{y}(t_T)\big)$; distribution over states at time $t_n - \Delta t$ given the present and future measurements.

Implemented in LinearModel.

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