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 */
027public interface ProcessModel {
028    /**
029     * Returns the state transition matrix.
030     *
031     * @return the state transition matrix
032     */
033    RealMatrix getStateTransitionMatrix();
034
035    /**
036     * Returns the control matrix.
037     *
038     * @return the control matrix
039     */
040    RealMatrix getControlMatrix();
041
042    /**
043     * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
044     * prediction step, so implementations of this interface may return a modified process noise
045     * depending on the current iteration step.
046     *
047     * @return the process noise matrix
048     * @see KalmanFilter#predict()
049     * @see KalmanFilter#predict(double[])
050     * @see KalmanFilter#predict(RealVector)
051     */
052    RealMatrix getProcessNoise();
053
054    /**
055     * Returns the initial state estimation vector.
056     * <p>
057     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
058     * state estimation with a zero vector.
059     *
060     * @return the initial state estimation vector
061     */
062    RealVector getInitialStateEstimate();
063
064    /**
065     * Returns the initial error covariance matrix.
066     * <p>
067     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
068     * error covariance with the process noise matrix.
069     *
070     * @return the initial error covariance matrix
071     */
072    RealMatrix getInitialErrorCovariance();
073}