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.extended;
19
20 import org.hipparchus.filtering.kalman.Measurement;
21 import org.hipparchus.linear.RealMatrix;
22 import org.hipparchus.linear.RealVector;
23
24 /**
25 * Non-linear process that can be estimated by a {@link ExtendedKalmanFilter}.
26 * <p>
27 * This interface must be implemented by users to represent the behavior
28 * of the process to be estimated
29 * </p>
30 * @param <T> the type of the measurements
31 * @see ExtendedKalmanFilter
32 * @see org.hipparchus.filtering.kalman.linear.LinearProcess
33 * @since 1.3
34 */
35 public interface NonLinearProcess<T extends Measurement> {
36
37 /** Get the state evolution between two times.
38 * @param previousTime time of the previous state
39 * @param previousState process state at {@code previousTime}
40 * @param measurement measurement to process
41 * @return state evolution
42 */
43 NonLinearEvolution getEvolution(double previousTime, RealVector previousState, T measurement);
44
45 /** Get the innovation brought by a measurement.
46 * @param measurement measurement to process
47 * @param evolution evolution returned by a previous call to {@link #getEvolution(double, RealVector, Measurement)}
48 * @param innovationCovarianceMatrix innovation covariance matrix, defined as \(h.P.h^T + r\)
49 * where h is the {@link NonLinearEvolution#getMeasurementJacobian() measurement Jacobian},
50 * P is the predicted covariance and r is {@link Measurement#getCovariance() measurement covariance}
51 * @return innovation brought by a measurement, may be null if measurement should be rejected
52 */
53 RealVector getInnovation(T measurement, NonLinearEvolution evolution, RealMatrix innovationCovarianceMatrix);
54
55 }