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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* 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
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
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 {@link KalmanFilter}.
*
* @since 3.0
*/
public interface ProcessModel {
/**
* Returns the state transition matrix.
*
* @return the state transition matrix
*/
RealMatrix getStateTransitionMatrix();
/**
* Returns the control matrix.
*
* @return the control matrix
*/
RealMatrix getControlMatrix();
/**
* 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.
*
* <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.
*
* <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();
}
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