ExtendedKalmanFilter.java

/*
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 * contributor license agreements.  See the NOTICE file distributed with
 * 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
 *
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * See the License for the specific language governing permissions and
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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);

        if (getObserver() != null) {
            getObserver().updatePerformed(this);
        }

        return getCorrected();

    }

}