Пример #1
0
	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;
        }
Пример #3
0
        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);
        }