KalmanFilter
import { KalmanFilter } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods.
example: samples/cpp/snippets/kalman.cpp An example using the standard Kalman filter example: samples/python/snippets/kalman.py An example using the standard Kalman filter in Python. Kalman filter class.
The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [Welch95] . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality.
Note: In C API when CvKalman* kalmanFilter structure is not needed anymore, it should be released with cvReleaseKalman(&kalmanFilter)
Constructors and members
static new
Create an owned KalmanFilter object. Release native handles with using or delete().
new(dynamParams: number, measureParams: number, controlParams: number, _3: number): KalmanFilter;4 available overloads
new(): KalmanFilter;new(_0: number, _1: number): KalmanFilter;new(dynamParams: number, measureParams: number, controlParams: number): KalmanFilter;new(dynamParams: number, measureParams: number, controlParams: number, _3: number): KalmanFilter;_00 argument (number).
_11 argument (number).
dynamParamsDimensionality of the state.
measureParamsDimensionality of the measurement.
controlParamsDimensionality of the control vector.
_3Type of the created matrices that should be CV_32F or CV_64F.
The KalmanFilter result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
statePre
predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)
statePre: Mat;statePost
corrected state (x(k)): x(k)=x'(k)+K(k)(z(k)-Hx'(k))
statePost: Mat;transitionMatrix
state transition matrix (A)
transitionMatrix: Mat;controlMatrix
control matrix (B) (not used if there is no control)
controlMatrix: Mat;measurementMatrix
measurement matrix (H)
measurementMatrix: Mat;processNoiseCov
process noise covariance matrix (Q)
processNoiseCov: Mat;measurementNoiseCov
measurement noise covariance matrix (R)
measurementNoiseCov: Mat;errorCovPre
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
errorCovPre: Mat;gain
Kalman gain matrix (K(k)): K(k)=P'(k)Htinv(H*P'(k)*Ht+R)
gain: Mat;errorCovPost
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
errorCovPost: Mat;predict
Computes a predicted state.
predict(control: Mat): Mat;controlThe optional input control
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
correct
Updates the predicted state from the measurement.
correct(measurement: Mat): Mat;measurementThe measured system parameters
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.