LMedSRobustRangingRadioSourceEstimator2D.java
/*
* Copyright (C) 2018 Alberto Irurueta Carro (alberto@irurueta.com)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.irurueta.navigation.indoor.radiosource;
import com.irurueta.geometry.Point2D;
import com.irurueta.navigation.LockedException;
import com.irurueta.navigation.NotReadyException;
import com.irurueta.navigation.indoor.RadioSource;
import com.irurueta.navigation.indoor.RangingReadingLocated;
import com.irurueta.numerical.robust.LMedSRobustEstimator;
import com.irurueta.numerical.robust.LMedSRobustEstimatorListener;
import com.irurueta.numerical.robust.RobustEstimator;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.List;
/**
* Robustly estimated 2D position of a radio source (e.g. Wi-Fi
* access point or bluetooth beacon), by discarding outliers using LMedS
* algorithm.
*
* @param <S> a {@link RadioSource} type.
*/
public class LMedSRobustRangingRadioSourceEstimator2D<S extends RadioSource> extends
RobustRangingRadioSourceEstimator2D<S> {
/**
* Default value to be used for stop threshold. Stop threshold can be used to
* avoid keeping the algorithm unnecessarily iterating in case that best
* estimated threshold using median of residuals is not small enough. Once a
* solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
public static final double DEFAULT_STOP_THRESHOLD = 1e-4;
/**
* Minimum allowed stop threshold value.
*/
public static final double MIN_STOP_THRESHOLD = 0.0;
/**
* Threshold to be used to keep the algorithm iterating in case that best
* estimated threshold using median of residuals is not small enough. Once
* a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
private double stopThreshold = DEFAULT_STOP_THRESHOLD;
/**
* Constructor.
*/
public LMedSRobustRangingRadioSourceEstimator2D() {
super();
}
/**
* Constructor.
* Sets radio signal ranging readings belonging to the same radio source.
*
* @param readings radio signal ranging readings belonging to the same
* radio source.
* @throws IllegalArgumentException if readings are not valid.
*/
public LMedSRobustRangingRadioSourceEstimator2D(final List<? extends RangingReadingLocated<S, Point2D>> readings) {
super(readings);
}
/**
* Constructor.
*
* @param listener listener in charge of attending events raised by this instance.
*/
public LMedSRobustRangingRadioSourceEstimator2D(
final RobustRangingRadioSourceEstimatorListener<S, Point2D> listener) {
super(listener);
}
/**
* Constructor.
* Sets radio signal readings belonging to the same radio source.
*
* @param readings radio signal readings belonging to the same radio source.
* @param listener listener in charge of attending events raised by this instance.
* @throws IllegalArgumentException if readings are not valid.
*/
public LMedSRobustRangingRadioSourceEstimator2D(
final List<? extends RangingReadingLocated<S, Point2D>> readings,
final RobustRangingRadioSourceEstimatorListener<S, Point2D> listener) {
super(readings, listener);
}
/**
* Constructor.
*
* @param initialPosition initial position to start the estimation or radio
* source position.
*/
public LMedSRobustRangingRadioSourceEstimator2D(final Point2D initialPosition) {
super(initialPosition);
}
/**
* Constructor.
* Sets radio signal readings belonging to the same radio source.
*
* @param readings radio signal readings belonging to the same radio source.
* @param initialPosition initial position to start the estimation of radio
* source position.
* @throws IllegalArgumentException if readings are not valid.
*/
public LMedSRobustRangingRadioSourceEstimator2D(
final List<? extends RangingReadingLocated<S, Point2D>> readings, final Point2D initialPosition) {
super(readings, initialPosition);
}
/**
* Constructor.
*
* @param initialPosition initial position to start the estimation of radio
* source position.
* @param listener listener in charge of attending events raised by this instance.
*/
public LMedSRobustRangingRadioSourceEstimator2D(
final Point2D initialPosition, final RobustRangingRadioSourceEstimatorListener<S, Point2D> listener) {
super(initialPosition, listener);
}
/**
* Constructor.
* Sets radio signal ranging readings belonging to the same radio source.
*
* @param readings radio signal ranging readings belonging to the same radio source.
* @param initialPosition initial position to start the estimation of radio source
* position.
* @param listener listener in charge of attending events raised by this instance.
* @throws IllegalArgumentException if readings are not valid.
*/
public LMedSRobustRangingRadioSourceEstimator2D(
final List<? extends RangingReadingLocated<S, Point2D>> readings, final Point2D initialPosition,
final RobustRangingRadioSourceEstimatorListener<S, Point2D> listener) {
super(readings, initialPosition, listener);
}
/**
* Returns threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm to iterate
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*
* @return stop threshold to stop the algorithm prematurely when a certain
* accuracy has been reached.
*/
public double getStopThreshold() {
return stopThreshold;
}
/**
* Sets threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value,
* the algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm to iterate
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*
* @param stopThreshold stop threshold to stop the algorithm prematurely
* when a certain accuracy has been reached.
* @throws IllegalArgumentException if provided value is zero or negative.
* @throws LockedException if this solver is locked.
*/
public void setStopThreshold(final double stopThreshold) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (stopThreshold <= MIN_STOP_THRESHOLD) {
throw new IllegalArgumentException();
}
this.stopThreshold = stopThreshold;
}
/**
* Robustly estimates position for a radio source.
*
* @throws LockedException if instance is busy during estimation.
* @throws NotReadyException if estimator is not ready.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
@SuppressWarnings("DuplicatedCode")
@Override
public void estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<Solution<Point2D>>() {
@Override
public int getTotalSamples() {
return readings.size();
}
@Override
public int getSubsetSize() {
return Math.max(preliminarySubsetSize, getMinReadings());
}
@Override
public void estimatePreliminarSolutions(
final int[] sampleIndices, final List<Solution<Point2D>> solutions) {
solvePreliminarySolutions(sampleIndices, solutions);
}
@Override
public double computeResidual(final Solution<Point2D> currentEstimation, final int i) {
return residual(currentEstimation, i);
}
@Override
public boolean isReady() {
return LMedSRobustRangingRadioSourceEstimator2D.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Solution<Point2D>> estimator) {
// no action needed
}
@Override
public void onEstimateEnd(final RobustEstimator<Solution<Point2D>> estimator) {
// no action needed
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<Solution<Point2D>> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(
LMedSRobustRangingRadioSourceEstimator2D.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<Solution<Point2D>> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(
LMedSRobustRangingRadioSourceEstimator2D.this, progress);
}
}
});
try {
locked = true;
if (listener != null) {
listener.onEstimateStart(this);
}
inliersData = null;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
final var result = innerEstimator.estimate();
inliersData = innerEstimator.getInliersData();
attemptRefine(result);
if (listener != null) {
listener.onEstimateEnd(this);
}
} catch (final com.irurueta.numerical.LockedException e) {
throw new LockedException(e);
} catch (final com.irurueta.numerical.NotReadyException e) {
throw new NotReadyException(e);
} finally {
locked = false;
}
}
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.LMEDS;
}
}