#include <KFCore.hpp>
Public Member Functions | |
| Base::vectorref | correct (typename Base::outputref y_meas) |
| template<class NewMeasVector > | |
| Base::vectorref | correct (typename Base::outputref measurements, NewMeasVector &newMeas, bool reject_outliers=true) |
| KFCore () | |
| void | predict (typename Base::inputref u=typename Model::input_type()) |
Private Types | |
| typedef KFBase< Model > | Base |
Detailed Description
template<class Model>
class labust::navigation::KFCore< Model >
This class combines models with different prediction and correction approaches.
- Todo:
- Switch to dynamic polymorphism for easier multi-model execution ?
Definition at line 53 of file KFCore.hpp.
Member Typedef Documentation
typedef KFBase< Model > labust::navigation::KFCore< Model >::Base [private] |
Definition at line 55 of file KFCore.hpp.
Constructor & Destructor Documentation
| labust::navigation::KFCore< Model >::KFCore | ( | ) | [inline] |
Generic constructor.
Definition at line 60 of file KFCore.hpp.
Member Function Documentation
| Base::vectorref labust::navigation::KFCore< Model >::correct | ( | typename Base::outputref | y_meas | ) | [inline] |
State estimate correction based on the available measurements. Note that the user has to handle the measurement processing and populate the y_meas vector.
- Returns:
- Corrected state estimate.
Calculate the Kalman gain matrix
K(k) = P_(k)*H(k)'*inv(H(k)*P(k)*H(k)' + V(k)*R(k)*V(k)');
Update the estimate covariance matrix. P(k) = (I - K(k)*H(k))*P(k);
Definition at line 90 of file KFCore.hpp.
| Base::vectorref labust::navigation::KFCore< Model >::correct | ( | typename Base::outputref | measurements, |
| NewMeasVector & | newMeas, | ||
| bool | reject_outliers = true |
||
| ) | [inline] |
Update the matrices V and H to accommodate for available measurements. Perform per element outlier rejection and correct the state with the remaining validated measurements.
- Returns:
- Corrected state estimate
- Todo:
- Remove this after debugging
Definition at line 131 of file KFCore.hpp.
| void labust::navigation::KFCore< Model >::predict | ( | typename Base::inputref | u = typename Model::input_type() | ) | [inline] |
Perform the prediction step based on the system input.
- Parameters:
-
u Input vector.
Forward the model in time and update the needed matrices: x_(k) = f(x(k-1),u(k),0)
Update the innovation matrix: P_(k) = A(k)*P(k-1)*A'(k) + W(k)*Q(k-1)*W'(k)
Definition at line 67 of file KFCore.hpp.
The documentation for this class was generated from the following file: