    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    }