ExtendedKalmanFilter.java
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* this work for additional information regarding copyright ownership.
* The Hipparchus project licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
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*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
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package org.hipparchus.filtering.kalman.extended;
import org.hipparchus.exception.MathRuntimeException;
import org.hipparchus.filtering.kalman.AbstractKalmanFilter;
import org.hipparchus.filtering.kalman.Measurement;
import org.hipparchus.filtering.kalman.ProcessEstimate;
import org.hipparchus.linear.MatrixDecomposer;
import org.hipparchus.linear.RealMatrix;
import org.hipparchus.linear.RealVector;
/**
* Kalman filter for {@link NonLinearProcess non-linear process}.
* @param <T> the type of the measurements
* @since 1.3
*/
public class ExtendedKalmanFilter<T extends Measurement> extends AbstractKalmanFilter<T> {
/** Process to be estimated. */
private final NonLinearProcess<T> process;
/** Simple constructor.
* @param decomposer decomposer to use for the correction phase
* @param process non-linear process to estimate
* @param initialState initial state
*/
public ExtendedKalmanFilter(final MatrixDecomposer decomposer,
final NonLinearProcess<T> process,
final ProcessEstimate initialState) {
super(decomposer, initialState);
this.process = process;
}
/** {@inheritDoc} */
@Override
public ProcessEstimate estimationStep(final T measurement)
throws MathRuntimeException {
// prediction phase
final NonLinearEvolution evolution = process.getEvolution(getCorrected().getTime(),
getCorrected().getState(),
measurement);
final RealMatrix stm = evolution.getStateTransitionMatrix();
predict(evolution.getCurrentTime(), evolution.getCurrentState(),
stm, evolution.getProcessNoiseMatrix());
// correction phase
final RealMatrix h = evolution.getMeasurementJacobian();
final RealMatrix s = computeInnovationCovarianceMatrix(measurement.getCovariance(), h);
final RealVector innovation = (h == null) ? null : process.getInnovation(measurement, evolution, s);
correct(measurement, stm, innovation, h, s);
return getCorrected();
}
}