indii::ml::filter::KalmanFilterModel

KalmanFilterModel Class Template Reference

Inheritance diagram for KalmanFilterModel:

Inheritance graph
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List of all members.

Detailed Description

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

KalmanFilter 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 KalmanFilterModel.hpp.


Public Member Functions

virtual ~KalmanFilterModel ()=0
 Destructor.
virtual
indii::ml::aux::GaussianPdf 		p_xtnp1_ytn (const indii::ml::aux::GaussianPdf &p_xtn_ytn, const T delta)=0
 Predict next system state.
virtual
indii::ml::aux::GaussianPdf 		p_xtnp1_ytnp1 (const indii::ml::aux::GaussianPdf &p_xtnp1_ytn, const indii::ml::aux::vector &ytnp1, const T delta)=0
 Refine prediction of next system state using next measurement.
virtual
indii::ml::aux::GaussianPdf 		p_y_x (const indii::ml::aux::GaussianPdf &p_x)=0
 Predict measurement from system state.

Constructor & Destructor Documentation

~KalmanFilterModel (  )  [inline, pure virtual]

Destructor.

Definition at line 85 of file KalmanFilterModel.hpp.

Member Function Documentation

virtual indii::ml::aux::GaussianPdf p_xtnp1_ytn ( const indii::ml::aux::GaussianPdf &  p_xtn_ytn,
const T  delta 
) [pure virtual]

Predict next system state.

Parameters:
p_xtn_ytn $P\big(\mathbf{x}(t_n)\, | \,\mathbf{y}(t_1),\ldots,\mathbf{y}(t_n)\big)$; distribution over states at the current time given the history of measurements.
delta $\Delta t$; time step.
Returns:
$P\big(\mathbf{x}(t_n + \Delta t)\, | \,\mathbf{y}(t_1),\ldots,\mathbf{y}(t_n)\big)$; predicted distribution over states at time $t_n + \Delta t$ given the history of measurements.

Implemented in LinearModel.

virtual indii::ml::aux::GaussianPdf p_xtnp1_ytnp1 ( const indii::ml::aux::GaussianPdf &  p_xtnp1_ytn,
const indii::ml::aux::vector &  ytnp1,
const T  delta 
) [pure virtual]

Refine prediction of next system state using next measurement.

Parameters:
p_xtnp1_ytn $P\big(\mathbf{x}(t_n + \Delta t)\, | \,\mathbf{y}(t_1),\ldots,\mathbf{y}(t_n)\big)$; predicted distribution over states at time $t_n + \Delta t$ given the history of measurements. Typically obtained from prior call to p_xtnp1_ytn.
ytnp1 $\mathbf{y}(t_n + \Delta t)$; the measurement at time $t_n + \Delta t$.
delta $\Delta t$; time step.
Returns:
$P\big(\mathbf{x}(t_n + \Delta t)\,|\,\mathbf{y}(t_1),\ldots,\mathbf{y}(t_n + \Delta t)\big)$; distribution over states at time $t_n + \Delta t$ given the history of measurements and new measurement.

Implemented in LinearModel.

virtual indii::ml::aux::GaussianPdf p_y_x ( const indii::ml::aux::GaussianPdf &  p_x  )  [pure virtual]

Predict measurement from system state.

Parameters:
p_x Arbitrary distribution over system state.
Returns:
Distribution over measurements given the system state.

Implemented in LinearModel.

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