Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

TL;DR

This paper presents a deep learning approach using GANs to generate synthetic fingerprint data, significantly reducing the need for extensive real data collection while maintaining high localization accuracy.

Contribution

The study introduces a novel method employing GANs to augment fingerprint localization data, reducing data collection costs without sacrificing accuracy.

Findings

Using 10% real data and 90% synthetic data achieves similar accuracy to full real data.

Synthetic data generation reduces data collection costs by up to 90%.

The approach maintains high localization performance with limited real data.

Abstract

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowdsourced data collection, or the use of semi-supervised algorithms. However, semi-supervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength (RSS) or channel state information (CSI) in wireless sensor networks to localize users in indoor/outdoor environments. In this paper, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reduce data-collection costs while achieving acceptable accuracy.