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value of TOA is calculated from the distance between the RP and the mobile. It is also possible to calculate RSS from the distance between the RP and the mobile by using path-loss models and avoid time-consuming measurements. Since path-loss models relating distance to RSS are statistical, RSS metrics are not reliable and RSS measurement has proven to provide a more practical solution [Bah00a, Bah00b]. This argument leads to the point that one may think of using statistical models with measurements to improve performance. This approach can reduce the number of measurements or increase the accuracy with the same number of measurements. In this approach, referred to as a statistical approach, we assume that RSS in any training location (xi , yi ) is a random variable p with a density function f [p/(xi , yi )] that is drawn from the samples of measurements performed at that location with certain assumptions. Then for a new measurement, RSS = P at an unknown location (x, y), we can calculate the probability of having that measurement taken in the speci c training points (xi , yi ): pi = f p/(xi , yi ) If we have N training points associated with every measured RSS at an unknown location, we will have N locations (xi , yi ) and N probabilities pi for the unknown location to be one of the training points. Then our actual estimate of the unknown location (x, y) is the statistical average over all measured training locations: asp.net code 39 reader .NET Code-39 Barcode Reader for C#, VB.NET, ASP.NET Applications
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