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fixed bad bug in finding head-up/bear-away clusters (swapped max for min); consider head-up/bear-away clusters for jibe cluster likelihood

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Axel Uhl committed 2015-01-27 17:24:50 +01:00
1 parent 8d551d8284
commit 04775d34f0
2 files changed
+36 -22
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java/com.sap.sailing.gwt.ui/src/main/java/com/sap/sailing/gwt/ui/client/shared/racemap/RaceMap.java
+1 -1
View File
@@ -674,7 +674,7 @@ public class RaceMap extends AbsolutePanel implements TimeListener, CompetitorSe
WindTrackInfoDTO windTrackInfoDTO = windInfo.windTrackInfoByWindSource.get(windSource);
switch (windSource.getType()) {
case EXPEDITION:
// we filter out measured wind sources with a very little confidence
// we filter out measured wind sources with vary low confidence
if (windTrackInfoDTO.minWindConfidence > 0.0001) {
windSourcesToShow.add(new com.sap.sse.common.Util.Pair<WindSource, WindTrackInfoDTO>(windSource, windTrackInfoDTO));
}
java/com.sap.sailing.polars/src/com/sap/sailing/polars/windestimation/ManeuverBasedWindEstimationTrackImpl.java
+35 -21
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@@ -77,7 +77,7 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
/**
* When one or two jibe clusters have been found whose
* {@link #getLikelihoodOfBeingJibeCluster(Speed, Stream, BoatClass) likelihood of being a jibe cluster} is 100%,
* {@link #getLikelihoodOfBeingJibeCluster(Speed, Stream, BoatClass, Set) likelihood of being a jibe cluster} is 100%,
* this is the boost factor by which the base likelihood for the tack cluster candidate will be increased (up to a
* maximum of 1.0).
*/
@@ -104,6 +104,14 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
* the jibe cluster may be off from the reversed tack cluster's centroid in order to be accepted as jibe cluster candidate.
*/
private static final double THRESHOLD_JIBE_CLUSTER_DIFFERENCE_DEGREES = 20;
/**
* For judging the likelihood of a cluster being a speed cluster, the speed ratio of the tack cluster candidate and
* the jibe cluster candidate is computed and compared to what the polar service predicts. The basic likelihood of the
* jibe cluster candidate (based on the maneuver angles relative to their speeds) is boosted by this factor for a perfect
* speed ratio match.
*/
private static final double BOOST_FACTOR_FOR_JIBE_TACK_SPEED_RATIO_LIKELIHOOD = 0;
private final PolarDataService polarService;
@@ -337,7 +345,7 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
* for that maneuver being a tack, solely based on its speed and maneuver angle, as
* {@link PolarDataService#getManeuverLikelihoodAndTwsTwa(BoatClass, Speed, double, ManeuverType) judged by the
* polar service}. If a cluster exists that has approximately the opposite average middle COG (the jibe cluster
* candidate), the {@link #getLikelihoodOfBeingJibeCluster(Speed, Stream, BoatClass) likelihood for its maneuvers
* candidate), the {@link #getLikelihoodOfBeingJibeCluster(Speed, Stream, BoatClass, Set) likelihood for its maneuvers
* being jibes} are determined and may result in a "boost" for the basic probability of up to 20%. Finally, the
* resulting likelihood is multiplied by how likely the speed ratio between the tack and jibe cluster candidates is,
* based on the polar diagram. For this task, the cluster's maneuvers' average speed (weighted by each maneuver's
@@ -360,8 +368,7 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
* maneuver angle dimension that show low variance each, compared to all other clusters.
* <p>
*
* TODO with enough candidates at hand, apply temporal and spatial segmentation to account for heterogeneous wind
* fields
* TODO with enough candidates at hand, apply temporal and spatial segmentation to account for heterogeneous wind fields
*/
private Triple<Set<Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble>>,
List<Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble>>,
@@ -491,9 +498,11 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
// Now search for head-up/bear-away cluster(s) to port and to starboard of cluster's weighted middle COG
final Bearing expectedUpwindStarboardTackCOG = averageUpwindCOG.add(new DegreeBearingImpl(-averageTackingAngleDeg/2.));
double starboardHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(cluster, clusters, expectedUpwindStarboardTackCOG);
double starboardHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(
clusters.stream().filter(c->c!=cluster), expectedUpwindStarboardTackCOG);
final Bearing expectedUpwindPortTackCOG = averageUpwindCOG.add(new DegreeBearingImpl(averageTackingAngleDeg/2.));
double portHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(cluster, clusters, expectedUpwindPortTackCOG);
double portHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(
clusters.stream().filter(c->c!=cluster), expectedUpwindPortTackCOG);
// under the assumption that cluster holds tacks, find the clusters that then most likely hold the jibes
final Bearing approximateMiddleCOGForJibes = averageUpwindCOG.reverse();
@@ -505,12 +514,12 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
if (tackClusterWeightedAverageSpeed != null) {
// find out how likely the speed ratio is between the candidate tack cluster and the corresponding
// hypothetical jibe clusters
double tackJibeLikelihoodBasedOnSpeedRatioAndAverageManeuverAngle = getLikelihoodOfBeingJibeCluster(
double jibeClusterLikelihood = getLikelihoodOfBeingJibeCluster(
tackClusterWeightedAverageSpeed, jibeClusters.map((jc) -> jc.stream()).reduce(Stream::concat)
.orElse(Stream.empty()), getBoatClass());
.orElse(Stream.empty()), getBoatClass(), clusters);
// Likely jibe clusters may raise the general likelihood by up to 20% of the value so far, but not to over 1.0
result = Math.min(1.0, averageTackLikelihood * likelihoodOfOppositeCluster *
(1.0 + BOOST_FACTOR_FOR_FULL_JIBE_CLUSTER_LIKELIHOOD * tackJibeLikelihoodBasedOnSpeedRatioAndAverageManeuverAngle +
(1.0 + BOOST_FACTOR_FOR_FULL_JIBE_CLUSTER_LIKELIHOOD * jibeClusterLikelihood +
BOOST_FACTOR_FOR_FULL_HEAD_UP_BEAR_AWAY_CLUSTER_LIKELIHOOD * starboardHeadUpBearAwayClusterLikelihood +
BOOST_FACTOR_FOR_FULL_HEAD_UP_BEAR_AWAY_CLUSTER_LIKELIHOOD * portHeadUpBearAwayClusterLikelihood));
} else {
@@ -527,11 +536,10 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
* @see #getLikelihoodOfClusterBasedOnDistanceFromExpected(double, Bearing, Cluster)
*/
private double getLikelihoodOfBestFittingHeadUpBearAwayCluster(
Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble> cluster,
Set<Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble>> clusters,
Stream<Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble>> clusters,
final Bearing expectedAverageMiddleCOG) {
final Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble> starboardTackHeadUpAndBearAwayCluster =
clusters.stream().filter((c)->c!=cluster).min((a, b)->(int) Math.signum(
clusters.max((a, b)->(int) Math.signum(
getLikelihoodOfClusterBasedOnDistanceFromExpected(EXPECTED_AVERAGE_HEAD_UP_AND_BEAR_AWAY_MANEUVER_ANGLE_DEG, expectedAverageMiddleCOG, a)-
getLikelihoodOfClusterBasedOnDistanceFromExpected(EXPECTED_AVERAGE_HEAD_UP_AND_BEAR_AWAY_MANEUVER_ANGLE_DEG, expectedAverageMiddleCOG, b))).
orElse(null);
@@ -650,13 +658,14 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
private double getLikelihoodOfBeingJibeCluster(
Speed tackClusterWeightedAverageSpeed,
Stream<ManeuverClassification> jibeClustersContent,
BoatClass boatClass) {
BoatClass boatClass, Set<Cluster<ManeuverClassification, Pair<ScalableBearing, ScalableDouble>, Pair<Bearing, Double>, ScalableBearingAndScalableDouble>> clusters) {
final int[] count = new int[1];
final double[] likelihoodSum = new double[1];
final ScalableBearing[] scaledAverageDownwindCOG = new ScalableBearing[1];
final double[] scaledAbsJibingAngleSum = new double[1];
Stream<ManeuverClassification> jibeClustersContentPeeker = jibeClustersContent.peek((mc)->{
count[0]++;
final Double likelihood = mc.getLikelihoodAndTWSBasedOnSpeedAndAngle(ManeuverType.JIBE).getA();
final double likelihood = mc.getLikelihoodAndTWSBasedOnSpeedAndAngle(ManeuverType.JIBE).getA();
likelihoodSum[0] += likelihood;
final ScalableBearing scaledCOG = new ScalableBearing(mc.getMiddleManeuverCourse()).multiply(likelihood);
if (scaledAverageDownwindCOG[0] == null) {
@@ -664,9 +673,11 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
} else {
scaledAverageDownwindCOG[0] = scaledAverageDownwindCOG[0].add(scaledCOG);
}
scaledAbsJibingAngleSum[0] += likelihood * Math.abs(mc.getManeuverAngleDeg());
});
Speed jibeClusterWeightedAverageSpeed = getWeightedAverageSpeed(jibeClustersContentPeeker, ManeuverType.JIBE);
Bearing averageDownwindCOG = scaledAverageDownwindCOG[0] == null ? null : scaledAverageDownwindCOG[0].divide(likelihoodSum[0]);
double absWeightedAverageJibingAbgle = scaledAbsJibingAngleSum[0] / likelihoodSum[0];
final double result;
if (jibeClusterWeightedAverageSpeed != null) {
double tackJibeSpeedRatioLikelihood = polarService.getConfidenceForTackJibeSpeedRatio(
@@ -674,13 +685,16 @@ public class ManeuverBasedWindEstimationTrackImpl extends WindTrackImpl {
if (count[0] > 0) {
double averageJibeLikelihood = likelihoodSum[0] / count[0];
// Now search for head-up/bear-away cluster(s) to port and to starboard of cluster's weighted middle COG
// TODO continue here...
// final Bearing expectedUpwindStarboardTackCOG = averageDownwindCOG.add(new DegreeBearingImpl(-averageJibingAngleDeg/2.));
// double starboardHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(cluster, clusters, expectedUpwindStarboardTackCOG);
// final Bearing expectedUpwindPortTackCOG = averageDownwindCOG.add(new DegreeBearingImpl(averageJibingAngleDeg/2.));
// double portHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(cluster, clusters, expectedUpwindPortTackCOG);
result = averageJibeLikelihood * tackJibeSpeedRatioLikelihood;
final Bearing expectedDownwindStarboardTackCOG = averageDownwindCOG.add(new DegreeBearingImpl(+absWeightedAverageJibingAbgle/2.));
double starboardHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(
clusters.stream(), expectedDownwindStarboardTackCOG);
final Bearing expectedDownwindPortTackCOG = averageDownwindCOG.add(new DegreeBearingImpl(-absWeightedAverageJibingAbgle/2.));
double portHeadUpBearAwayClusterLikelihood = getLikelihoodOfBestFittingHeadUpBearAwayCluster(
clusters.stream(), expectedDownwindPortTackCOG);
result = Math.min(1.0, averageJibeLikelihood
* (1.0 + BOOST_FACTOR_FOR_JIBE_TACK_SPEED_RATIO_LIKELIHOOD * tackJibeSpeedRatioLikelihood +
BOOST_FACTOR_FOR_FULL_HEAD_UP_BEAR_AWAY_CLUSTER_LIKELIHOOD * starboardHeadUpBearAwayClusterLikelihood +
BOOST_FACTOR_FOR_FULL_HEAD_UP_BEAR_AWAY_CLUSTER_LIKELIHOOD * portHeadUpBearAwayClusterLikelihood));
} else {
throw new RuntimeException("Internal error: no maneuvers in jibe cluster candidate but still a valid weighted average speed "+jibeClusterWeightedAverageSpeed);
}