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SE-MTCAELoc: SE-Aided Multi-Task Convolutional Autoencoder for Indoor Localization with Wi-Fi.

Yongfeng Li1,2,3, Juan Huang2, Yuan Yao1,2

  • 1Faculty of Data Science, City University of Macau, Macau, China.

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|February 13, 2026
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Summary

This study introduces the SE-MTCAELoc model for accurate indoor localization, improving Wi-Fi fingerprinting by integrating a squeeze-excitation (SE) attention mechanism with a convolutional autoencoder (CAE). The model achieves high accuracy in building and floor classification and precise coordinate regression, outperforming traditional methods in complex environments.

Keywords:
RSSISE attention mechanismWiFi fingerprintconvolutional autoencoderindoor localizationmulti-task learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geographic Information Science

Background:

  • Traditional Wi-Fi fingerprinting for indoor localization faces challenges with signal interference and generalization across different indoor environments.
  • Complex indoor scenarios, including multi-building and multi-floor settings, hinder the performance of existing localization techniques.

Purpose of the Study:

  • To develop an advanced indoor localization model, SE-MTCAELoc, that overcomes the limitations of traditional methods by integrating a squeeze-excitation (SE) attention mechanism with a convolutional autoencoder (CAE).
  • To enhance the accuracy, robustness, and generalization capabilities of indoor positioning systems in diverse and challenging indoor environments.

Main Methods:

  • The SE-MTCAELoc model preprocesses Wi-Fi Received Signal Strength (RSSI) data, augmenting and reshaping it into matrices, and introduces Gaussian noise for improved data robustness.
  • An integrated SE module combined with a convolutional autoencoder (CAE) is employed to aggregate spatial information and dynamically enhance key positioning features while suppressing noise.
  • A multi-task learning architecture is utilized to jointly optimize building classification, floor classification, and coordinate regression, with weighted losses prioritizing coordinate accuracy.

Main Results:

  • The SE-MTCAELoc model achieved high accuracy on the UJIIndoorLoc dataset: 99.57% for building classification, 98.57% for floor classification, and a Mean Absolute Error (MAE) of 5.23 m for coordinate regression.
  • On the TUT2018 dataset, the model demonstrated strong performance with 98.13% floor classification accuracy and an MAE of 6.16 m.
  • The model exhibits exceptional time efficiency, with a cumulative training duration of 9.83 minutes and single-sample inference in just 0.347 milliseconds, meeting real-time application demands.

Conclusions:

  • The SE-MTCAELoc model effectively enhances indoor localization accuracy and generalization ability in complex indoor scenarios, addressing limitations of traditional Wi-Fi fingerprinting methods.
  • The integration of the SE attention mechanism within the CAE framework significantly improves feature extraction and noise suppression for more reliable positioning.
  • The model's multi-task learning approach and efficient processing capabilities make it suitable for a wide range of real-time indoor localization applications across multiple environments.