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

Hongxiu Zhao¹, Wafa Njima¹, Xun Zhang¹

¹Department of Telecommunication Engineering, Institut Supérieur d'Electronique de Paris (ISEP), 92130 Paris, France.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This study introduces a Continual Learning (CL) system for improved localization accuracy in dynamic environments. The CL approach enhances both new and old data accuracy, overcoming limitations of Transfer Learning (TL) and catastrophic forgetting.

Keywords:

continual learning (CL)dynamic environment fingerprinting rehearsal transfer learning (TL)

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Area of Science:

Robotics
Machine Learning
Wireless Sensor Networks

Background:

Localization accuracy degrades in dynamic environments due to outdated databases.
Traditional Transfer Learning (TL) methods struggle with catastrophic forgetting, losing performance on previous data when learning new data.

Purpose of the Study:

To propose a novel fingerprint-based Continual Learning (CL) localization system.
To enhance localization accuracy for both new and old data while mitigating catastrophic forgetting.

Main Methods:

The proposed system utilizes a Continual Learning (CL) approach for fingerprint-based localization.
It involves rehearsing parameters in lower network layers and reducing the training rate in upper layers to balance learning new and retaining old information.

Main Results:

The CL system demonstrated significant accuracy improvements over TL.
In smaller rooms, accuracy improved by 16% for new data and 29% for old data.
In larger rooms, accuracy improved by 14% for new data and 44% for old data.

Conclusions:

The proposed Continual Learning (CL) approach effectively enhances localization accuracy in dynamic environments.
This method successfully mitigates catastrophic forgetting, a key limitation of Transfer Learning (TL).