001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    package org.apache.commons.math3.filter;
018    
019    import org.apache.commons.math3.linear.RealMatrix;
020    import org.apache.commons.math3.linear.RealVector;
021    
022    /**
023     * Defines the process dynamics model for the use with a {@link KalmanFilter}.
024     *
025     * @since 3.0
026     * @version $Id: ProcessModel.java 1416643 2012-12-03 19:37:14Z tn $
027     */
028    public interface ProcessModel {
029        /**
030         * Returns the state transition matrix.
031         *
032         * @return the state transition matrix
033         */
034        RealMatrix getStateTransitionMatrix();
035    
036        /**
037         * Returns the control matrix.
038         *
039         * @return the control matrix
040         */
041        RealMatrix getControlMatrix();
042    
043        /**
044         * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
045         * prediction step, so implementations of this interface may return a modified process noise
046         * depending on the current iteration step.
047         *
048         * @return the process noise matrix
049         * @see KalmanFilter#predict()
050         * @see KalmanFilter#predict(double[])
051         * @see KalmanFilter#predict(RealVector)
052         */
053        RealMatrix getProcessNoise();
054    
055        /**
056         * Returns the initial state estimation vector.
057         * <p>
058         * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
059         * state estimation with a zero vector.
060         *
061         * @return the initial state estimation vector
062         */
063        RealVector getInitialStateEstimate();
064    
065        /**
066         * Returns the initial error covariance matrix.
067         * <p>
068         * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
069         * error covariance with the process noise matrix.
070         *
071         * @return the initial error covariance matrix
072         */
073        RealMatrix getInitialErrorCovariance();
074    }