#include <SSModel.hpp>

| Public Types | |
| typedef Eigen::Matrix < precission, Eigen::Dynamic, Eigen::Dynamic > | matrix | 
| typedef precission | numericprecission | 
| typedef Eigen::Matrix < precission, Eigen::Dynamic, 1 > | vector | 
| Public Member Functions | |
| void | setState (vector x) | 
| SSModel () | |
| Public Attributes | |
| matrix | A | 
| matrix | B | 
| matrix | H | 
| matrix | H0 | 
| matrix | Q | 
| matrix | R | 
| matrix | R0 | 
| precission | Ts | 
| matrix | V | 
| matrix | V0 | 
| matrix | W | 
| Protected Attributes | |
| vector | x | 
| vector | xk_1 | 
This class implements a generic stochastic state space model. When implementing models try to inherit from this class. The following naming scheme is used:
x(k+1) = A*x(k) + B*u(k) + w(k) y(k) = H*x(k) + v(k)
w - model noise p(w) = N(0,Q(k)) v - measurement noise p(v) = N(0,R(k))
In the nonlinear case:
x(k+1) = f(x(k),u(k),w(k)) y(k) = h(x(k),v(k))
update the following jacobian matrices as: A = {{f}}{{x}}(x(k-1),u(k),0) W = {{f}}{{w}}(x(k-1),u(k),0) H = {{h}}{{x}}(x(k),0) V = {{h}}{{v}}(x(k),0)
We leave the matrix size generic for now. Usually these could be supplied directly in the template.
Definition at line 71 of file SSModel.hpp.
| typedef Eigen::Matrix<precission, Eigen::Dynamic, Eigen::Dynamic> labust::navigation::SSModel< precission >::matrix | 
Definition at line 76 of file SSModel.hpp.
| typedef precission labust::navigation::SSModel< precission >::numericprecission | 
Definition at line 74 of file SSModel.hpp.
| typedef Eigen::Matrix<precission, Eigen::Dynamic, 1> labust::navigation::SSModel< precission >::vector | 
Definition at line 77 of file SSModel.hpp.
| labust::navigation::SSModel< precission >::SSModel | ( | ) |  [inline] | 
Main constructor.
Definition at line 86 of file SSModel.hpp.
| void labust::navigation::SSModel< precission >::setState | ( | vector | x | ) |  [inline] | 
Sets the internal state of the model.
Definition at line 90 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::A | 
State transition, Input, Model noise covariance, Model noise transformation Measurement noise covariance, Measurement noise transformation
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::B | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::H | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::H0 | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::Q | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::R | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::R0 | 
Definition at line 100 of file SSModel.hpp.
| precission labust::navigation::SSModel< precission >::Ts | 
Model sampling time
Definition at line 104 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::V | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::V0 | 
Definition at line 100 of file SSModel.hpp.
| matrix labust::navigation::SSModel< precission >::W | 
Definition at line 100 of file SSModel.hpp.
| vector labust::navigation::SSModel< precission >::x  [protected] | 
Definition at line 110 of file SSModel.hpp.
| vector labust::navigation::SSModel< precission >::xk_1  [protected] | 
State and output vector.
Definition at line 110 of file SSModel.hpp.