commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/filter/ProcessModel.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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 */
package org.apache.commons.math4.legacy.filter;

import org.apache.commons.math4.legacy.linear.RealMatrix;
import org.apache.commons.math4.legacy.linear.RealVector;

/**
 * Defines the process dynamics model for the use with a {@link KalmanFilter}.
 *
 * @since 3.0
 */
public interface ProcessModel {
    /**
     * Returns the state transition matrix.
     *
     * @return the state transition matrix
     */
    RealMatrix getStateTransitionMatrix();

    /**
     * Returns the control matrix.
     *
     * @return the control matrix
     */
    RealMatrix getControlMatrix();

    /**
     * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
     * prediction step, so implementations of this interface may return a modified process noise
     * depending on the current iteration step.
     *
     * @return the process noise matrix
     * @see KalmanFilter#predict()
     * @see KalmanFilter#predict(double[])
     * @see KalmanFilter#predict(RealVector)
     */
    RealMatrix getProcessNoise();

    /**
     * Returns the initial state estimation vector.
     * <p>
     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
     * state estimation with a zero vector.
     *
     * @return the initial state estimation vector
     */
    RealVector getInitialStateEstimate();

    /**
     * Returns the initial error covariance matrix.
     * <p>
     * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
     * error covariance with the process noise matrix.
     *
     * @return the initial error covariance matrix
     */
    RealMatrix getInitialErrorCovariance();
}