# Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies

## TL;DR

This paper presents an AI-based calibration framework that significantly improves the accuracy of low-cost indoor localisation technologies by leveraging highly accurate systems, requiring minimal measurement effort and computational complexity.

## Contribution

It introduces a technology-agnostic neural network calibration method that enhances low-cost localisation accuracy using high-precision systems, suitable for constrained devices.

## Key findings

- Approximately 70% accuracy improvement for BLE
- About 50% accuracy improvement for UWB
- Requires short measurement campaigns

## Abstract

High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people. However, these technologies can be used to lend their highly accurate localisation capabilities to low-cost, commodity, and less-accurate technologies. In this paper, we bridge this link by proposing a technology-agnostic calibration framework based on artificial intelligence to assist such low-cost technologies through highly accurate localisation systems. A single-layer neural network is used to calibrate less accurate technology using more accurate one such as BLE using UWB and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an increase in accuracy of approximately 70% for BLE and 50% for UWB. Not only the proposed approach requires a very short measurement campaign, the low complexity of the single-layer neural network also makes it ideal for deployment on constrained devices typically for localisation purposes.