# Unsupervised Learning Technique to Obtain the Coordinates of Wi-Fi Access Points

## TL;DR

This paper introduces an unsupervised learning method that automatically identifies Wi-Fi access point locations and calibrates distance measurements, enhancing indoor positioning accuracy without ground truth data.

## Contribution

It presents a novel unsupervised approach for estimating Wi-Fi access point coordinates and calibration curves, improving indoor positioning without requiring prior ground truth.

## Key findings

- Accurately estimated access point locations in a practical indoor environment.
- Effectively learned calibration curves to correct distance measurement distortions.
- Enhanced positioning accuracy with more anchor nodes and calibration.

## Abstract

Given that the accuracy of range-based positioning techniques generally increases with the number of available anchor nodes, it is important to secure more of these nodes. To this end, this paper studies an unsupervised learning technique to obtain the coordinates of unknown nodes that coexist with anchor nodes. As users use the location services in an area of interests, the proposed method automatically discovers unknown nodes and estimates their coordinates. In addition, this method learns an appropriate calibration curve to correct the distortion of raw distance measurements. As such, the positioning accuracy can be greatly improved using more anchor nodes and well-calibrated distance measurements. The performance of the proposed method was verified using commercial Wi-Fi devices in a practical indoor environment. The experiment results show that the coordinates of unknown nodes and the calibration curve are simultaneously determined without any ground truth data.