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ipft (version 0.6)

Indoor Positioning Fingerprinting Toolset

Description

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.

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Version

Install

install.packages('ipft')

Monthly Downloads

188

Version

0.6

License

GPL (>= 2)

Maintainer

Emilio Sansano

Last Published

May 10th, 2017

Functions in ipft (0.6)

Creates groups based on the specified parameters

Implements the k-nearest neighbors algorithm

Creates clusters using the specified method

Distance function

Plots the cumulative distribution function of the estimated error

Plots the estimated locations

Estimates the positions of the access points

Estimates the location of the test observations

This function implements a probabilistic algorithm

Estimates the position of the observations from its fingerprints and the access point location usins a logarithmic path loss model

Indoor localization test data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Indoor localization training data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Plots the spatial location of the observations

Plots the probability density function of the estimated error

Transform function

Indoor localization data set with the positions of the wireless access points present in the ipftrain and ipftest data sets. Unknown locations are stored as NAs. Data from the positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).