1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17 package org.apache.commons.math4.legacy.filter;
18
19 import org.apache.commons.math4.legacy.linear.RealMatrix;
20 import org.apache.commons.math4.legacy.linear.RealVector;
21
22 /**
23 * Defines the process dynamics model for the use with a {@link KalmanFilter}.
24 *
25 * @since 3.0
26 */
27 public interface ProcessModel {
28 /**
29 * Returns the state transition matrix.
30 *
31 * @return the state transition matrix
32 */
33 RealMatrix getStateTransitionMatrix();
34
35 /**
36 * Returns the control matrix.
37 *
38 * @return the control matrix
39 */
40 RealMatrix getControlMatrix();
41
42 /**
43 * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
44 * prediction step, so implementations of this interface may return a modified process noise
45 * depending on the current iteration step.
46 *
47 * @return the process noise matrix
48 * @see KalmanFilter#predict()
49 * @see KalmanFilter#predict(double[])
50 * @see KalmanFilter#predict(RealVector)
51 */
52 RealMatrix getProcessNoise();
53
54 /**
55 * Returns the initial state estimation vector.
56 * <p>
57 * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
58 * state estimation with a zero vector.
59 *
60 * @return the initial state estimation vector
61 */
62 RealVector getInitialStateEstimate();
63
64 /**
65 * Returns the initial error covariance matrix.
66 * <p>
67 * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the
68 * error covariance with the process noise matrix.
69 *
70 * @return the initial error covariance matrix
71 */
72 RealMatrix getInitialErrorCovariance();
73 }