Class LMedSRobustMixedPositionEstimator2D
java.lang.Object
com.irurueta.navigation.indoor.position.RobustPositionEstimator<com.irurueta.geometry.Point2D,Reading<? extends RadioSource>,RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D>>
com.irurueta.navigation.indoor.position.RobustMixedPositionEstimator<com.irurueta.geometry.Point2D>
com.irurueta.navigation.indoor.position.RobustMixedPositionEstimator2D
com.irurueta.navigation.indoor.position.LMedSRobustMixedPositionEstimator2D
Robustly estimates 2D position using located radio sources and their readings at
unknown locations and using LMedS algorithm to discard outliers.
This kind of estimator can be used to robustly determine the 2D position of a given
device by getting readings at an unknown location of different radio sources whose
2D locations are known.
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Field Summary
Fields inherited from class com.irurueta.navigation.indoor.position.RobustPositionEstimator
DEFAULT_EVENLY_DISTRIBUTE_READINGS, DEFAULT_ROBUST_METHOD, DEFAULT_USE_RADIO_SOURCE_POSITION_COVARIANCE, FALLBACK_DISTANCE_STANDARD_DEVIATION, fingerprint, laterationSolver, listener, preliminarySubsetSize, sources, trilaterationSolverListener -
Constructor Summary
ConstructorsConstructorDescriptionConstructor.LMedSRobustMixedPositionEstimator2D(Fingerprint<? extends RadioSource, ? extends Reading<? extends RadioSource>> fingerprint) Constructor.LMedSRobustMixedPositionEstimator2D(Fingerprint<? extends RadioSource, ? extends Reading<? extends RadioSource>> fingerprint, RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D> listener) Constructor.LMedSRobustMixedPositionEstimator2D(RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D> listener) Constructor.LMedSRobustMixedPositionEstimator2D(List<? extends RadioSourceLocated<com.irurueta.geometry.Point2D>> sources) Constructor.LMedSRobustMixedPositionEstimator2D(List<? extends RadioSourceLocated<com.irurueta.geometry.Point2D>> sources, Fingerprint<? extends RadioSource, ? extends Reading<? extends RadioSource>> fingerprint) Constructor.LMedSRobustMixedPositionEstimator2D(List<? extends RadioSourceLocated<com.irurueta.geometry.Point2D>> sources, Fingerprint<? extends RadioSource, ? extends Reading<? extends RadioSource>> fingerprint, RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D> listener) Constructor.LMedSRobustMixedPositionEstimator2D(List<? extends RadioSourceLocated<com.irurueta.geometry.Point2D>> sources, RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D> listener) Constructor. -
Method Summary
Modifier and TypeMethodDescriptioncom.irurueta.numerical.robust.RobustEstimatorMethodReturns method being used for robust estimation.doubleReturns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.private voidinit()Initializes robust lateration solver.voidsetStopThreshold(double 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.Methods inherited from class com.irurueta.navigation.indoor.position.RobustMixedPositionEstimator2D
create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, getMinRequiredSources, setPositionsDistancesDistanceStandardDeviationsAndQualityScoresMethods inherited from class com.irurueta.navigation.indoor.position.RobustPositionEstimator
buildPositionsDistancesDistanceStandardDeviationsAndQualityScores, estimate, getConfidence, getCovariance, getDistances, getDistanceStandardDeviations, getEstimatedPosition, getEvenlyDistributeReadings, getFallbackDistanceStandardDeviation, getFingerprint, getFingerprintReadingsQualityScores, getInitialPosition, getInliersData, getListener, getMaxIterations, getNumberOfDimensions, getPositions, getPreliminarySubsetSize, getProgressDelta, getSourceQualityScores, getSources, internalSetFingerprint, internalSetSources, isCovarianceKept, isHomogeneousLinearSolverUsed, isLinearSolverUsed, isLocked, isPreliminarySolutionRefined, isRadioSourcePositionCovarianceUsed, isReady, isResultRefined, setConfidence, setCovarianceKept, setEvenlyDistributeReadings, setFallbackDistanceStandardDeviation, setFingerprint, setFingerprintReadingsQualityScores, setHomogeneousLinearSolverUsed, setInitialPosition, setLinearSolverUsed, setListener, setMaxIterations, setPreliminarySolutionRefined, setPreliminarySubsetSize, setProgressDelta, setRadioSourcePositionCovarianceUsed, setResultRefined, setSourceQualityScores, setSources
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Constructor Details
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LMedSRobustMixedPositionEstimator2D
public LMedSRobustMixedPositionEstimator2D()Constructor. -
LMedSRobustMixedPositionEstimator2D
public LMedSRobustMixedPositionEstimator2D(List<? extends RadioSourceLocated<com.irurueta.geometry.Point2D>> sources) Constructor.- Parameters:
sources- located radio sources used for lateration.- Throws:
IllegalArgumentException- if provided sources is null or the number of provided sources is less than the required minimum.
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Method Details
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getStopThreshold
public double getStopThreshold()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.- Returns:
- stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.
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setStopThreshold
public void setStopThreshold(double stopThreshold) throws com.irurueta.navigation.LockedException 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.- Parameters:
stopThreshold- stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.- Throws:
IllegalArgumentException- if provided value is zero or negative.com.irurueta.navigation.LockedException- if this solver is locked.
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getMethod
public com.irurueta.numerical.robust.RobustEstimatorMethod getMethod()Returns method being used for robust estimation.- Specified by:
getMethodin classRobustPositionEstimator<com.irurueta.geometry.Point2D,Reading<? extends RadioSource>, RobustMixedPositionEstimatorListener<com.irurueta.geometry.Point2D>> - Returns:
- method being used for robust estimation.
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init
private void init()Initializes robust lateration solver.
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