ipft v0.6


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by Emilio Sansano

Indoor Positioning Fingerprinting Toolset

Algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data sets, estimation of positions,comparison of the performance of different models, and graphical visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or probabilistic fingerprinting are implemented in this package to perform analysis and estimations over RSSI data sets.

Functions in ipft

Name Description
ipfGroup Creates groups based on the specified parameters
ipfKnn Implements the k-nearest neighbors algorithm
ipfCluster Creates clusters using the specified method
ipfDist Distance function
ipfPlotEcdf Plots the cumulative distribution function of the estimated error
ipfPlotEst Plots the estimated locations
ipfEstbp Estimates the positions of the access points
ipfEstimate Estimates the location of the test observations
ipfProb This function implements a probabilistic algorithm
ipfProx Estimates the position of the observations from its fingerprints and the access point location
ipftest Indoor localization test data set to test Indoor Positioning System that
ipftrain Indoor localization training data set to test Indoor Positioning System that
ipfPlotLoc Plots the spatial location of the observations
ipfPlotPdf Plots the probability density function of the estimated error
ipfTransform Transform function
ipfpwap Indoor localization data set with the positions of the wireless access points present
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Type Package
License GPL (>= 2)
Encoding UTF-8
LazyData true
RoxygenNote 6.0.1
LinkingTo Rcpp
NeedsCompilation yes
Packaged 2017-05-08 14:13:00 UTC; emilio
Repository CRAN
Date/Publication 2017-05-10 12:28:25 UTC

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