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java.lang.Objectkalman.CvKalman
public class CvKalman
Kalman filter (state).
The structure CvKalman is used to keep Kalman filter state. It is created by constructor function, updated by cvKalmanPredict and cvKalmanCorrect functions.
Normally, the structure is used for standard Kalman filter (notation and the formulae below are borrowed from the excellent Kalman tutorial [Welch95]):
xk=A*xk-1+B*uk+wk zk=Hxk+vk,
where:
xk (xk-1) - state of the system at the moment k (k-1) zk - measurement of the system state at the moment k uk - external control applied at the moment k wk and vk are normally-distributed process and measurement noise, respectively: p(w) ~ N(0,Q) p(v) ~ N(0,R), that is, Q - process noise covariance matrix, constant or variable, R - measurement noise covariance matrix, constant or variable
In case of standard Kalman filter, all the matrices: A, B, H, Q and R are initialized once after CvKalman structure is allocated via constructor. However, the same structure and the same functions may be used to simulate extended Kalman filter by linearizing extended Kalman filter equation in the current system state neighborhood, in this case A, B, H (and, probably, Q and R) should be updated on every step.
Constructor Summary | |
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CvKalman(int dynam_params,
int measure_params)
Constructor in case of no control. |
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CvKalman(int dynam_params,
int measure_params,
int control_params)
The function cvCreateKalman allocates CvKalman and all its matrices and initializes them somehow. |
Method Summary | |
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CvMat |
cvKalmanCorrect(CvMat measurement)
Adjusts model state. |
CvMat |
cvKalmanPredict(CvMat control)
Estimates subsequent model state. |
static void |
main(java.lang.String[] args)
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Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public CvKalman(int dynam_params, int measure_params, int control_params) throws java.lang.Exception
dynam_params
- measure_params
- control_params
-
java.lang.Exception
- Kalman Exception.public CvKalman(int dynam_params, int measure_params) throws java.lang.Exception
dynam_params
- measure_params
-
java.lang.Exception
Method Detail |
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public CvMat cvKalmanPredict(CvMat control)
The function cvKalmanPredict
estimates the subsequent
stochastic model state by its current state and stores it at
kalman->state_pre
:
x'k=A*xk+B*uk
P'k=A*Pk-1*AT + Q,
where
x'k is predicted state (kalman->state_pre),
xk-1 is corrected state on the previous step (kalman->state_post)
(should be initialized somehow in the beginning, zero vector by default),
uk is external control (control
parameter),
P'k is priori error covariance matrix (kalman->error_cov_pre)
Pk-1 is posteriori error covariance matrix on the previous step (kalman->error_cov_post)
(should be initialized somehow in the beginning, identity matrix by default),
control
- Control vector (uk), should be NULL if there
is no external control (control_params
=0).
public CvMat cvKalmanCorrect(CvMat measurement)
const CvMat* cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement ); #define cvKalmanUpdateByMeasurement cvKalmanCorrect
The function cvKalmanCorrect
adjusts stochastic model state on the
basis of the given measurement of the model state:
Kk=P'k*HT*(H*P'k*HT+R)-1
xk=x'k+Kk*(zk-H*x'k)
Pk=(I-Kk*H)*P'k
where
zk - given measurement (mesurement
parameter)
Kk - Kalman "gain" matrix.
The function stores adjusted state at kalman->state_post
and returns it on output.
measurement
-
public static void main(java.lang.String[] args)
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