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 package org.hipparchus.filtering.kalman.unscented;
18
19 import org.hipparchus.filtering.kalman.Measurement;
20 import org.hipparchus.linear.RealMatrix;
21 import org.hipparchus.linear.RealVector;
22
23 /**
24 * Unscented process that can be estimated by a {@link UnscentedKalmanFilter}.
25 * <p>
26 * This interface must be implemented by users to represent the behavior
27 * of the process to be estimated
28 * </p>
29 * @param <T> the type of the measurements
30 * @see UnscentedKalmanFilter
31 * @see org.hipparchus.filtering.kalman.unscented.UnscentedProcess
32 * @since 2.2
33 */
34 public interface UnscentedProcess<T extends Measurement> {
35
36 /** Get the state evolution between two times.
37 * @param previousTime time of the previous state
38 * @param sigmaPoints sigma points
39 * @param measurement measurement to process
40 * @return states evolution
41 */
42 UnscentedEvolution getEvolution(double previousTime, RealVector[] sigmaPoints, T measurement);
43
44 /** Get the state evolution between two times.
45 * @param predictedSigmaPoints predicted state sigma points
46 * @param measurement measurement to process
47 * @return predicted measurement sigma points
48 */
49 RealVector[] getPredictedMeasurements(RealVector[] predictedSigmaPoints, T measurement);
50
51 /** Get the innovation brought by a measurement.
52 * @param measurement measurement to process
53 * @param predictedMeasurement predicted measurement
54 * @param predictedState predicted state
55 * @param innovationCovarianceMatrix innovation covariance matrix
56 * @return innovation brought by a measurement, may be null if measurement should be rejected
57 */
58 RealVector getInnovation(T measurement, RealVector predictedMeasurement, RealVector predictedState, RealMatrix innovationCovarianceMatrix);
59
60 }