Transfer Learning for Indoor Localization Algorithm Based on Deep Domain Adaptation.

Area of Science:

• Computer Science
• Signal Processing
• Machine Learning

Background:

• Indoor positioning systems face accuracy degradation due to varying environmental conditions and signal drift.
• Existing algorithms struggle with inconsistent data distributions between training and testing datasets.
• Current transfer learning methods offer limited domain adaptation due to shallow feature extraction.

Purpose of the Study:

• To address the accuracy instability of indoor positioning algorithms under non-ideal conditions.
• To improve domain adaptation in indoor localization by leveraging deep learning's feature extraction capabilities.
• To propose a novel deep learning network for robust indoor positioning.

Main Methods:

• A Deep Joint Mean Distribution Adaptation Network (DJMDAN) was developed.
• The network utilizes deep networks for enhanced feature extraction.
• DJMDAN aligns global and subdomain distributions across different data domains.

Main Results:

• The proposed DJMDAN method demonstrated superior performance in indoor positioning applications.
• The approach effectively reduces domain discrepancy by learning more transferable features.
• Experimental results show significant improvement over comparison algorithms.

Conclusions:

• Deep learning-based domain adaptation offers a promising solution for indoor positioning challenges.
• DJMDAN effectively mitigates accuracy reduction caused by data distribution shifts.
• The proposed method enhances the reliability and feasibility of indoor location algorithms.