PCDA-MPAT: Physically Constrained Data Augmentation and Multipath-Aware Transformer for Ambient IoT Indoor Localization

XIAO Shanpeng; FAN Yijing; ZHENG Shiying; CAO Yanyan; WANG Yamin; XING Fei

doi:10.12263/DZXB.20260456

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PCDA-MPAT: Physically Constrained Data Augmentation and Multipath-Aware Transformer for Ambient IoT Indoor Localization

RF Computing | 更新时间：2026-10-16

- PCDA-MPAT: Physically Constrained Data Augmentation and Multipath-Aware Transformer for Ambient IoT Indoor Localization
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2522-2533(2026)
- 作者机构：
  
  1.清华大学精密仪器系，北京 100084
  2.中国移动通信研究院，北京 100053
- 作者简介：
- 基金信息：
  
  National Science and Technology Major Projects of China(2025ZD1301400)
- DOI：10.12263/DZXB.20260456
  CLC： TP391;TP183
- Received：08 May 2026，
  
  Accepted：01 June 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
肖善鹏, 范艺晶, 郑师应, 等. PCDA-MPAT：物理约束数据增强与多径Transformer的无源物联网室内定位方法[J]. 电子学报, 2026, 54(06): 2522-2533.

XIAO Shanpeng, FAN Yijing, ZHENG Shiying, et al. PCDA-MPAT: Physically Constrained Data Augmentation and Multipath-Aware Transformer for Ambient IoT Indoor Localization[J]. Acta Electronica Sinica, 2026, 54(06): 2522-2533.
肖善鹏, 范艺晶, 郑师应, 等. PCDA-MPAT：物理约束数据增强与多径Transformer的无源物联网室内定位方法[J]. 电子学报, 2026, 54(06): 2522-2533. DOI：10.12263/DZXB.20260456

XIAO Shanpeng, FAN Yijing, ZHENG Shiying, et al. PCDA-MPAT: Physically Constrained Data Augmentation and Multipath-Aware Transformer for Ambient IoT Indoor Localization[J]. Acta Electronica Sinica, 2026, 54(06): 2522-2533. DOI：10.12263/DZXB.20260456

摘要

无源物联网（Ambient Internet of Things，Ambient IoT）室内定位技术在智慧仓储、新零售等场景中具有重要应用价值，但室内复杂多径环境会引发两方面突出问题：一是多径遮挡和标签性能差异导致强弱标签样本严重不均衡，在典型服装陈列场景中，弱标签有效样本量仅为强标签的30%左右；二是多径混叠使信号的位置相关特征区分度显著下降，制约了定位精度的提升。为解决上述问题，本文提出一种融合物理约束数据增强与多径感知Transformer（Physically Constrained Data Augmentation and MultiPath-Aware Transformer，PCDA-MPAT）的定位方法。在数据增强层面，本文提出物理约束的数据增强（Physically Constrained Data Augmentation，PCDA）方法，以相位-频率斜率一致性作为物理约束条件，构建带有梯度惩罚的条件Wasserstein生成对抗网络（Wasserstein Generative Adversarial Network with Gradient Penalty，WGAN-GP），生成既匹配实测数据分布又符合无线传播物理特性的弱标签样本，从数据层面有效缓解了强弱标签样本不均衡问题。在模型设计层面，本文构建多径感知Transformer（MultiPath-Aware Transformer，MPAT）定位模型，设计适配准静态多径信道慢衰落特性的时域局部窗口注意力机制，结合时域相对位置编码，捕捉多径特征的时域演化规律，抑制长时域不相关的噪声干扰，增强模型对多径特征的提取与学习能力。在10 m × 10 m的服装陈列场景中开展实测实验，结果表明本文方法的标签级定位准确率达到83.57%，较现有最优基线方法提升5.71个百分点。消融实验进一步验证了PCDA与MPAT的有效性，其中，PCDA使标签级定位准确率提升4.29个百分点，MPAT在此基础上进一步提升2.14个百分点。上述结果验证了本文方法在复杂室内多径环境下的有效性，可满足智慧仓储、新零售等场景的定位需求。

Abstract

Ambient internet of things (Ambient IoT) indoor localization technology holds significant application value in scenarios such as smart warehousing and new retail. However

complex indoor multipath environments give rise to two prominent issues: first

severe imbalance between strong and weak tag samples caused by multipath blockage and tag performance variations. In a typical clothing display scenario

the effective sample size for weak tags is only about 30% of that for strong tags. Second

multipath aliasing significantly degrades the distinguishability of location-dependent signal features

limiting improvements in localization accuracy. To address these issues

this paper proposes a physically constrained data augmentation and multipath-aware transformer (PCDA-MPAT) localization method that integrates physically constrained data augmentation with a multipath-aware Transformer. At the data augmentation level

we propose a physically constrained data augmentation (PCDA) method. Using phase-frequency slope consistency as a physical constraint

we construct a conditional Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate weak tag samples that not only match the distribution of measured data but also adhere to the physical laws of wireless propagation

effectively alleviating the imbalance between strong and weak tag samples from the data perspective. At the model design level

we construct a multipath-aware transformer (MPAT) localization model. We design a time-domain local window attention mechanism adapted to the slow-fading characteristics of quasi-static multipath channels

combined with time-domain relative position encoding

to capture the temporal evolution patterns of multipath features

suppress long-term irrelevant noise interference

and enhance the model’s ability to extract and learn multipath features. Experimental results in a 10 m×10 m clothing display scenario demonstrate that the proposed method achieves a tag-level localization accuracy of 83.57%

outperforming the best existing baseline method by 5.71 percentage points. Ablation studies further validate the effectiveness of PCDA and MPAT. PCDA alone improves the tag-level localization accuracy by 4.29 percentage points

and MPAT further increases it by 2.14 percentage points. These results confirm the effectiveness of the proposed method in complex indoor multipath environments

meeting the localization requirements of scenarios such as smart warehousing and new retail.

关键词

Keywords

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