Class KalmanFilter

Represents a Kalman filter state.

Inheritance
Object
CVHandle
KalmanFilter
Namespace: OpenCV.Net
Assembly: OpenCV.Net.dll
Syntax
public class KalmanFilter : CVHandle

Constructors

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KalmanFilter(Int32, Int32, Int32)

Initializes a new instance of the KalmanFilter class with the specified dimensionality.

Declaration
public KalmanFilter(int dynamParams, int measureParams, int controlParams)
Parameters
Type Name Description
Int32 dynamParams

Dimensionality of the state vector.
Int32 measureParams

Dimensionality of the measurement vector.
Int32 controlParams

Dimensionality of the control vector.

Properties

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ControlMatrix

Gets the control matrix (B). Not used if there is no control.

Declaration
public Mat ControlMatrix { get; }
Property Value
Type Description
Mat
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ErrorCovariancePosterior

Gets the posterior error estimate covariance matrix (P(k)): P(k) = (I - K(k) * H) * P'(k).

Declaration
public Mat ErrorCovariancePosterior { get; }
Property Value
Type Description
Mat
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ErrorCovariancePrior

Gets the prior error estimate covariance matrix (P'(k)): P'(k) = A * P(k - 1) * At + Q.

Declaration
public Mat ErrorCovariancePrior { get; }
Property Value
Type Description
Mat
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Gain

Gets the Kalman gain matrix (K(k)): K(k) = P'(k) * Ht * inv(H * P'(k) * Ht + R).

Declaration
public Mat Gain { get; }
Property Value
Type Description
Mat
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MeasurementMatrix

Gets the measurement matrix (H).

Declaration
public Mat MeasurementMatrix { get; }
Property Value
Type Description
Mat
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MeasurementNoiseCovariance

Gets the measurement noise covariance matrix (R).

Declaration
public Mat MeasurementNoiseCovariance { get; }
Property Value
Type Description
Mat
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ProcessNoiseCovariance

Gets the process noise covariance matrix (Q).

Declaration
public Mat ProcessNoiseCovariance { get; }
Property Value
Type Description
Mat
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StateCorrected

Gets the corrected state vector (x(k)): x(k) = x'(k) + K(k) * (z(k) - H * x'(k))

Declaration
public Mat StateCorrected { get; }
Property Value
Type Description
Mat
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StatePredicted

Gets the predicted state vector (x'(k)): x'(k) = A * x(k - 1) + B * u(k).

Declaration
public Mat StatePredicted { get; }
Property Value
Type Description
Mat
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TransitionMatrix

Gets the state transition matrix (A).

Declaration
public Mat TransitionMatrix { get; }
Property Value
Type Description
Mat

Methods

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Correct(Mat)

Adjusts the model state.

Declaration
public Mat Correct(Mat measurement)
Parameters
Type Name Description
Mat measurement

The measurement vector z(k).
Returns
Type Description
Mat

The adjusted state estimate.
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Predict()

Estimates the subsequent model state.

Declaration
public Mat Predict()
Returns
Type Description
Mat

The estimated state vector.
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Predict(Mat)

Estimates the subsequent model state.

Declaration
public Mat Predict(Mat control)
Parameters
Type Name Description
Mat control

The control vector u(k) or null if there is no control.
Returns
Type Description
Mat

The estimated state vector.
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ReleaseHandle()

Executes the code required to free the native KalmanFilter handle.

Declaration
protected override bool ReleaseHandle()
Returns
Type Description
Boolean

true if the handle is released successfully; otherwise, in the event of a catastrophic failure, false.