bdm::EKFfull Class Reference
Extended Kalman Filter in full matrices. More...
#include <kalman.h>
Inheritance diagram for bdm::EKFfull:
Public Member Functions | |
| EKFfull () | |
| Default constructor. | |
| void | set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< diffbifn > &phxu, const mat Q0, const mat R0) |
| Set nonlinear functions for mean values and covariance matrices. | |
| void | bayes (const vec &yt, const vec &cond=empty_vec) |
| Here dt = [yt;ut] of appropriate dimensions. | |
| void | set_statistics (const vec &mu0, const mat &P0) |
| set estimates | |
| const mat | _R () |
| access function | |
| void | from_setting (const Setting &set) |
| void | validate () |
Protected Attributes | |
| shared_ptr< diffbifn > | pfxu |
| Internal Model f(x,u). | |
| shared_ptr< diffbifn > | phxu |
| Observation Model h(x,u). | |
Detailed Description
Extended Kalman Filter in full matrices.An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean.
Member Function Documentation
| void bdm::EKFfull::from_setting | ( | const Setting & | set | ) | [inline, virtual] |
Create object from the following structure
class = 'EKFfull'; OM = configuration of bdm::diffbifn; % any offspring of diffbifn, bdm::diffbifn::from_setting IM = configuration of bdm::diffbifn; % any offspring of diffbifn, bdm::diffbifn::from_setting dQ = [...]; % vector containing diagonal of Q dR = [...]; % vector containing diagonal of R --- optional fields --- mu0 = [...]; % vector of statistics mu0 dP0 = [...]; % vector containing diagonal of P0 -- or -- P0 = [...]; % full matrix P0 --- inherited fields --- bdm::BM::from_setting
mu0 = [0,0,0,....]; % empty statistics
P0 = eye( dim );
Reimplemented from bdm::Kalman< sq_T >.
The documentation for this class was generated from the following files:
- kalman.h
- kalman.cpp
Generated on 2 Dec 2013 for mixpp by
1.4.7
