A Generalization Method for Indoor Localization via Domain-Invariant Feature Learning

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-02-13 DOI:10.1109/LCOMM.2025.3541653

Manyu Xue;Zhendong Xu;Jiankun Zhang;Hao Wang;Yuan Shen

引用次数: 0

Abstract

Deep learning methods have been widely applied in indoor localization to mitigate the impact of non-line-of-sight (NLOS) and multipath effects, achieving high-precision results. However, these methods have to integrate large amounts of data for training and often struggle to adapt to new and unseen scenarios. This letter proposes a domain generalization method based on domain adversarial training (DAT) and mutual information (MI) estimation, where cross-environment generalization localization is achieved by extracting domain-invariant features, eliminating the need for training data from new scenes. Experimental results demonstrate that the proposed method outperforms other deep learning approaches in terms of localization accuracy across various indoor environments.

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基于域不变特征学习的室内定位泛化方法

深度学习方法被广泛应用于室内定位，以减轻非视距（NLOS）和多径效应的影响，获得高精度的定位结果。然而，这些方法必须集成大量的训练数据，并且经常难以适应新的和看不见的场景。本文提出了一种基于域对抗训练（DAT）和互信息（MI）估计的域泛化方法，通过提取域不变特征实现跨环境泛化定位，从而消除了对新场景训练数据的需要。实验结果表明，该方法在不同室内环境下的定位精度优于其他深度学习方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.