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A Dual-Modality CNN Approach for RSS-Based Indoor Positioning Using Spatial and Frequency Fingerprints.

Xiangchen Lai1, Yunzhi Luo2, Yong Jia1

  • 1School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.

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|September 13, 2025
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Summary
This summary is machine-generated.

This study enhances indoor positioning using received signal strength (RSS) by combining spatial and frequency data. The novel method significantly improves accuracy, reducing positioning errors by over 47%.

Keywords:
convolutional neural networkcross-modalityfingerprinting indoor positioningradio signal strength

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Area of Science:

  • Indoor positioning systems
  • Wireless sensor networks
  • Signal processing

Background:

  • Received signal strength (RSS) based indoor positioning relies on spatial fingerprint features.
  • Low-resolution fingerprint images limit the extraction of subtle features, impacting accuracy and robustness.
  • Existing methods struggle with effectively utilizing spatial information for precise localization.

Purpose of the Study:

  • To propose an improved RSS-based indoor positioning method using enhanced spatial and frequency domain fingerprint representations.
  • To leverage fusion learning with a convolutional neural network (CNN) for more accurate 3D coordinate prediction.
  • To address the limitations of low-resolution spatial RSS fingerprint images.

Main Methods:

  • Applied bicubic interpolation to enhance spatial resolution of RSS fingerprint images.
  • Utilized 2D Fast Fourier Transform (2D FFT) to convert spatial images into frequency domain representations.
  • Developed a parallel CNN with efficient multi-scale attention (EMA) modules for dual-modality (spatial and frequency) feature extraction and fusion.
  • Implemented a coordinate optimization strategy for final 3D coordinate estimation.

Main Results:

  • The proposed method demonstrated significant improvements in positioning accuracy across seven public datasets.
  • Achieved a reduction in mean positioning error by up to 47.1%.
  • Reduced root mean square error (RMSE) by up to 54.4% compared to traditional and advanced methods.

Conclusions:

  • The combination of enhanced spatial-frequency fingerprint representation and fusion learning effectively extracts richer features for indoor positioning.
  • The proposed method offers a substantial advancement in RSS-based indoor positioning accuracy and robustness.
  • This approach provides a promising solution for overcoming the challenges posed by low-resolution spatial RSS fingerprint images.