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1   /*
2    * Licensed to the Hipparchus project 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 Hipparchus project 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    *      https://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  
18  package org.hipparchus.filtering.kalman.linear;
19  
20  import org.hipparchus.filtering.kalman.Measurement;
21  
22  /**
23   * Linear process that can be estimated by a {@link LinearKalmanFilter}.
24   * <p>
25   * This interface must be implemented by users to represent the behavior
26   * of the process to be estimated
27   * </p>
28   * <p>
29   * A linear process is governed by the equation:
30   * \(
31   *  x_k = A_{k-1} x_{k-1} + B_{k-1} u_{k-1} + w_{k-1}
32   * \)
33   * where</p>
34   * <ul>
35   *   <li>A<sub>k-1</sub> is the state transition matrix in the absence of control,</li>
36   *   <li>B<sub>k-1</sub> is the control matrix,</li>
37   *   <li>u<sub>k-1</sub> is the command</li>
38   *   <li>w<sub>k-1</sub> is the process noise, which has covariance matrix Q<sub>k-1</sub></li>
39   * </ul>
40   * @param <T> the type of the measurements
41   * @see LinearKalmanFilter
42   * @see org.hipparchus.filtering.kalman.extended.NonLinearProcess
43   * @since 1.3
44   */
45  public interface LinearProcess<T extends Measurement> {
46  
47      /** Get the state evolution between two times.
48       * @param measurement measurement to process
49       * @return state evolution
50       */
51      LinearEvolution getEvolution(T measurement);
52  
53  }