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Extended Kalman Filter (EKF) implementation. More...
#include <Kalman.h>
Public Member Functions | |
| KalmanEkf (int _n) | |
 Public Member Functions inherited from Kalman | |
| Kalman (int _n) | |
| Constructor. More... | |
| ~Kalman () | |
| Destructor. | |
| cv::Mat & | predict (unsigned long tick) |
| Predict the Kalman state vector for the given time step This calls updateF for updating the transition matrix based on the real time step. More... | |
| cv::Mat & | predict_update (KalmanSensor *sensor, unsigned long tick) |
| Predict the Kalman state vector for the given time step and update the state using the Kalman gain. More... | |
| double | seconds_since_update (unsigned long tick) |
| Helper method. | |
| Public Member Functions inherited from KalmanCore | |
| KalmanCore (const KalmanCore &s) | |
| Copy constructor. | |
| KalmanCore (int _n) | |
| Constructor. More... | |
| ~KalmanCore () | |
| Destructor. | |
| int | get_n () |
| Accessor for n. | |
| virtual cv::Mat & | predict () |
| Predict the Kalman state vector for the given time step . x_pred = F * x. | |
| cv::Mat & | predict_update (KalmanSensorCore *sensor) |
| Predict the Kalman state vector and update the state using the constant Kalman gain. x = x_pred + K* ( z - H*x_pred) | |
Protected Member Functions | |
| virtual void | f (const cv::Mat &_x, cv::Mat &_x_pred, double dt)=0 |
| virtual void | update_F (unsigned long tick) |
| virtual void | predict_x (unsigned long tick) |
| Protected Member Functions inherited from Kalman | |
| void | predict_P () |
Protected Attributes | |
| cv::Mat | delta |
| cv::Mat | x_plus |
| cv::Mat | x_minus |
| cv::Mat | x_tmp1 |
| cv::Mat | x_tmp2 |
| Protected Attributes inherited from Kalman | |
| int | prev_tick |
| Protected Attributes inherited from KalmanCore | |
| int | n |
| cv::Mat | F_trans |
Additional Inherited Members | |
| Public Attributes inherited from Kalman | |
| cv::Mat | P |
| The error covariance matrix describing the accuracy of the state estimate. | |
| cv::Mat | Q |
| The covariance matrix for the process noise. | |
| cv::Mat | P_pred |
| The predicted error covariance matrix. | |
| Public Attributes inherited from KalmanCore | |
| cv::Mat | x |
| The Kalman state vector (n*1) | |
| cv::Mat | F |
| The matrix (n*n) containing the transition model for the internal state. | |
| cv::Mat | x_pred |
| Predicted state, TODO: should be protected?! | |
Extended Kalman Filter (EKF) implementation.
Please override the pure virtual f() with the desired unlinear function. By default the upate_F calculates the Jacobian numerically, if you want other approach override also the update_F()
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protectedvirtual |
If your transition matrix F is based on time you need to override this method.
Reimplemented from Kalman.
1.9.1