# Fast Radio Map Construction and Position Estimation via Direct Mapping for WLAN Indoor Localization System

## TL;DR

This paper introduces a novel indoor WLAN localization method that significantly reduces fingerprinting effort by using direct mapping with metric learning, achieving high accuracy with minimal radio map data.

## Contribution

The paper presents a new direct mapping localization approach using metric learning and manifold alignment, reducing fingerprinting workload by up to 87% while maintaining high accuracy.

## Key findings

- Achieves less than 4.3 m mean localization error in office environments.
- Reduces fingerprinting workload by up to 87%.
- Room-level localization error within 9 m with over 99% accuracy.

## Abstract

The main limitation that constrains the fast and comprehensive application of Wireless Local Area Network (WLAN) based indoor localization systems with Received Signal Strength (RSS) positioning algorithms is the building of the fingerprinting radio map, which is time-consuming especially when the indoor environment is large and/or with high frequent changes. Different approaches have been proposed to reduce workload, including fingerprinting deployment and update efforts, but the performance degrades greatly when the workload is reduced below a certain level. In this paper, we propose an indoor localization scenario that applies metric learning and manifold alignment to realize direct mapping localization (DML) using a low resolution radio map with single sample of RSS that reduces the fingerprinting workload by up to 87\%. Compared to previous work. The proposed two localization approaches, DML and $k$ nearest neighbors based on reconstructed radio map (reKNN), were shown to achieve less than 4.3\ m and 3.7\ m mean localization error respectively in a typical office environment with an area of approximately 170\ m$^2$, while the unsupervised localization with perturbation algorithm was shown to achieve 4.7\ m mean localization error with 8 times more workload than the proposed methods. As for the room level localization application, both DML and reKNN can meet the requirement with at most 9\ m of localization error which is enough to tell apart different rooms with over 99\% accuracy.

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Source: https://tomesphere.com/paper/1703.06933