public EstimateResult getEstimate(SnifferWifiMeasurement currentMeasurement)
{
//Check ready state
if (mCurrentBuilding == null)
return null;
//Maintain primary and secondary search space
//Cf OfflineClientPocketPCUF
if (secondarySearchSpace == null)
{
secondarySearchSpace = mCurrentBuilding.Vertices; // mGraph.getVertices();
}
EstimateResult primaryEstimate = new EstimateResult(null, Double.MaxValue);
EstimateResult secondaryEstimate = new EstimateResult(null, Double.MaxValue);
BestCandidateSet = new BCS(5); //candidates are added in the compare methods below
//measurement is compared with primary search space (adjacent vertices to previous estimated vertex)
//and secondary search space (non-connected nodes or the full graph)
if (prevBestEstimateVertex != null)
{
primaryEstimate = mPosAlgorithm.compare(prevBestEstimateVertex.adjacentVertices(), currentMeasurement);
}
secondaryEstimate = mPosAlgorithm.compare(secondarySearchSpace, currentMeasurement);
//Changed to accomodate hyper, where we return null if online meas only has one mac
//Vertex best = null;
EstimateResult bestEstimate = null;
if (primaryEstimate != null)
bestEstimate = primaryEstimate;
if (secondaryEstimate != null)
{
//The primary estimate may be overriden by a secondary if it is better for the second time in a row
if (secondaryEstimate.getDistance() < primaryEstimate.getDistance())
{
numSecondaryBest++;
if (numSecondaryBest >= 2 || prevBestEstimateVertex == null)
{
numSecondaryBest = 0;
bestEstimate = secondaryEstimate; //.getVertex();
}
}
else
{
numSecondaryBest = 0;
}
}
prevBestEstimateVertex = bestEstimate.getVertex();
//Currently, the error estimate is also calculated in the compare methods,
//but we override that logic here since this implementation considers the global
//candidates - not just the local primary- or secondary candidates.
//We throw in an extra 5 meters to account for movement
double error = Math.Ceiling(BestCandidateSet.getMaxDistance()); // + 5;
bestEstimate.setErrorEstimate(error);
return bestEstimate;
}
public EstimateResult compare(IEnumerable <Vertex> vertices, SnifferWifiMeasurement measurement)
{
measurement = getNStrongestAPMeasurement(measurement, 7);
if (vertices == null || measurement == null)
{
return(null);
}
bcs = WifiPosEngine.BestCandidateSet; //new BCS(5); //bcs.clear();
double curDist; //distance of current vertice in search space
EstimateResult result = new EstimateResult(null, double.MaxValue);
foreach (Vertex curVertex in vertices) //sammenlign med hver Vertex
{
foreach (SnifferWifiMeasurement curFP in curVertex.SnifferWifiMeasurements) //sammenlign med hvert fingerprint (usually only one - otherwise use more intelligent approach)
{
curDist = 0;
foreach (String mac in measurement.getMACs()) //all APs in sample
{
if (curFP.containsMac(mac))
{
curDist += Math.Pow((measurement.getAvgDbM(mac) - curFP.getAvgDbM(mac)), 2);
}
else
{
curDist += Math.Pow((measurement.getAvgDbM(mac) - MISSING_MAC_PENALTY), 2);
}
}
curDist = Math.Sqrt(curDist);
if (curDist < result.getDistance())
{
result.setDistance(curDist);
result.setVertex(curVertex);
}
bcs.add(curVertex, curDist); //add to best candidate set - which will take care of only using the best estimates.
}
}
//The following only yields a local error estimate within the primary- or secondary
//vertices and may thus not be appropriate
result.setErrorEstimate(Math.Ceiling(bcs.getMaxDistance()));
return(result);
}
public EstimateResult compare(IEnumerable<Vertex> vertices, SnifferWifiMeasurement measurement)
{
measurement = getNStrongestAPMeasurement(measurement, 7);
if (vertices == null || measurement == null)
return null;
bcs = WifiPosEngine.BestCandidateSet; //new BCS(5); //bcs.clear();
double curDist; //distance of current vertice in search space
EstimateResult result = new EstimateResult(null, double.MaxValue);
foreach (Vertex curVertex in vertices) //sammenlign med hver Vertex
{
foreach (SnifferWifiMeasurement curFP in curVertex.SnifferWifiMeasurements) //sammenlign med hvert fingerprint (usually only one - otherwise use more intelligent approach)
{
curDist = 0;
foreach (String mac in measurement.getMACs()) //all APs in sample
if (curFP.containsMac(mac))
curDist += Math.Pow((measurement.getAvgDbM(mac) - curFP.getAvgDbM(mac)), 2);
else
curDist += Math.Pow((measurement.getAvgDbM(mac) - MISSING_MAC_PENALTY), 2);
curDist = Math.Sqrt(curDist);
if (curDist < result.getDistance())
{
result.setDistance(curDist);
result.setVertex(curVertex);
}
bcs.add(curVertex, curDist); //add to best candidate set - which will take care of only using the best estimates.
}
}
//The following only yields a local error estimate within the primary- or secondary
//vertices and may thus not be appropriate
result.setErrorEstimate(Math.Ceiling(bcs.getMaxDistance()));
return result;
}
public EstimateResult getEstimate(SnifferWifiMeasurement currentMeasurement)
{
//Check ready state
if (mCurrentBuilding == null)
{
return(null);
}
//Maintain primary and secondary search space
//Cf OfflineClientPocketPCUF
if (secondarySearchSpace == null)
{
secondarySearchSpace = mCurrentBuilding.Vertices; // mGraph.getVertices();
}
EstimateResult primaryEstimate = new EstimateResult(null, Double.MaxValue);
EstimateResult secondaryEstimate = new EstimateResult(null, Double.MaxValue);
BestCandidateSet = new BCS(5); //candidates are added in the compare methods below
//measurement is compared with primary search space (adjacent vertices to previous estimated vertex)
//and secondary search space (non-connected nodes or the full graph)
if (prevBestEstimateVertex != null)
{
primaryEstimate = mPosAlgorithm.compare(prevBestEstimateVertex.adjacentVertices(), currentMeasurement);
}
secondaryEstimate = mPosAlgorithm.compare(secondarySearchSpace, currentMeasurement);
//Changed to accomodate hyper, where we return null if online meas only has one mac
//Vertex best = null;
EstimateResult bestEstimate = null;
if (primaryEstimate != null)
{
bestEstimate = primaryEstimate;
}
if (secondaryEstimate != null)
{
//The primary estimate may be overriden by a secondary if it is better for the second time in a row
if (secondaryEstimate.getDistance() < primaryEstimate.getDistance())
{
numSecondaryBest++;
if (numSecondaryBest >= 2 || prevBestEstimateVertex == null)
{
numSecondaryBest = 0;
bestEstimate = secondaryEstimate; //.getVertex();
}
}
else
{
numSecondaryBest = 0;
}
}
prevBestEstimateVertex = bestEstimate.getVertex();
//Currently, the error estimate is also calculated in the compare methods,
//but we override that logic here since this implementation considers the global
//candidates - not just the local primary- or secondary candidates.
//We throw in an extra 5 meters to account for movement
double error = Math.Ceiling(BestCandidateSet.getMaxDistance()); // + 5;
bestEstimate.setErrorEstimate(error);
return(bestEstimate);
}