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Indoor location perception model based on Resnet50 and Elman network.

Pengjun Zhang1, Jie Mi2

  • 1School of Architectural Engineering, Hebei Vocational University of Industry and Technology, Shijiazhuang, China.

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This study enhances visible light indoor positioning by improving feature extraction with Resnet50 and optimizing Elman networks using a grey wolf algorithm. The new method achieves higher accuracy and stability for indoor location sensing.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Visible light indoor positioning offers high accuracy and low cost but suffers from environmental interference and signal instability.
  • Traditional methods struggle with diverse feature representation and model parameter optimization, leading to positioning errors.

Purpose of the Study:

  • To enhance the accuracy and stability of visible light indoor positioning.
  • To address limitations in signal anti-interference, feature representation, and model parameter optimization.

Main Methods:

  • Developed an image data feature extraction method using Resnet50 and feature pyramid concepts.
  • Designed an indoor location sensing model based on the Elman network.
  • Proposed an improved grey wolf optimization algorithm to optimize Elman network parameters.

Main Results:

  • The feature extraction method created a more diverse feature library (cosine similarity closer to 0).
  • The improved grey wolf optimization algorithm achieved lower average fitness values than comparison algorithms.
  • The designed perception model demonstrated average errors of 3.04cm, 3.57cm, and 3.19cm at different heights, outperforming comparison models.

Conclusions:

  • The proposed method significantly improves the diversity of feature representation.
  • The optimized Elman network model achieves high accuracy and stability in indoor positioning.
  • This research provides a viable technical pathway for advanced indoor location sensing.