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 }