Work

High-Accuracy and Real-Time Fingerprint-Based Continual Learning Localization System in Dynamic Environment

Year: 2025

Type: article

Abstract: In dynamic environments, localization accuracy often deteriorates due to an outdated or invalid database. Traditional approaches typically use Transfer Learning (TL) to address this issue, but TL suffers from the problem of catastrophic forgetting. This paper proposes a fingerprint-based Continual Learning (CL) localization system designed to retain old data while enhancing the accuracy for new data. The system works by rehearsing parameters in the lower network layers and reducing the training rate in the upper layers. Simulations conducted with fused data show that the proposed approach improves accuracy by 16% for new data and 29% for old data compared to TL in smaller rooms. In larger rooms, it achieves a 14% improvement for new data and a 44% improvement for old data over TL. These results demonstrate that the proposed CL approach not only enhances localization accuracy but also effectively mitigates the issue of catastrophic forgetting.

Source: Sensors

DOI: 10.3390/s25051289

Authors: Hongxiu Zhao, Wafa Njima, Xun Zhang

Institution: Institut Supérieur d'Électronique de Paris

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: China Scholarship Council, Horizon 2020 Framework Programme