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 */
017package org.apache.commons.math3.filter;
018
019import org.apache.commons.math3.linear.RealMatrix;
020import 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 */
028public 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}