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 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
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 *      http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.commons.math3.filter;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;

Defines the process dynamics model for the use with a KalmanFilter. 
Since:
3.0/**
 * Defines the process dynamics model for the use with a {@link KalmanFilter}.
 *
 * @since 3.0
 */
public interface ProcessModel {
    
Returns the state transition matrix.
Returns:
the state transition matrix/**
     * Returns the state transition matrix.
     *
     * @return the state transition matrix
     */
    RealMatrix getStateTransitionMatrix();

    
Returns the control matrix.
Returns:
the control matrix/**
     * Returns the control matrix.
     *
     * @return the control matrix
     */
    RealMatrix getControlMatrix();

    
Returns the process noise matrix. This method is called by the KalmanFilter every prediction step, so implementations of this interface may return a modified process noise depending on the current iteration step. 
See Also:
KalmanFilter.predict()
KalmanFilter.predict(double[])
KalmanFilter.predict(RealVector)
Returns:
the process noise matrix/**
     * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
     * prediction step, so implementations of this interface may return a modified process noise
     * depending on the current iteration step.
     *
     * @return the process noise matrix
     * @see KalmanFilter#predict()
     * @see KalmanFilter#predict(double[])
     * @see KalmanFilter#predict(RealVector)
     */
    RealMatrix getProcessNoise();

    
Returns the initial state estimation vector.

Note: if the return value is zero, the Kalman filter will initialize the
state estimation with a zero vector.
Returns:
the initial state estimation vector/**
     * Returns the initial state estimation vector.
     * <p>
     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
     * state estimation with a zero vector.
     *
     * @return the initial state estimation vector
     */
    RealVector getInitialStateEstimate();

    
Returns the initial error covariance matrix.

Note: if the return value is zero, the Kalman filter will initialize the
error covariance with the process noise matrix.
Returns:
the initial error covariance matrix/**
     * Returns the initial error covariance matrix.
     * <p>
     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
     * error covariance with the process noise matrix.
     *
     * @return the initial error covariance matrix
     */
    RealMatrix getInitialErrorCovariance();
}