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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization.

Bedionita Soro1, Chaewoo Lee2

  • 1Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea. sorobedio@ajou.ac.kr.

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Summary
This summary is machine-generated.

This study introduces a new feature extraction method using wavelet scattering transform for Artificial Neural Networks (ANNs) in indoor localization. The method enhances localization accuracy by stabilizing features against signal variations.

Keywords:
feature extractionfingerprintingindoor localizationindoor positioningwavelet scattering

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Indoor Localization

Background:

  • Artificial Neural Network (ANN) performance in indoor fingerprinting is hindered by unstable Received Signal Strength Indicator (RSSI) variations.
  • Existing feature extraction methods inadequately address RSSI fluctuations, degrading ANN-based indoor localization accuracy.

Purpose of the Study:

  • To develop a robust feature extraction technique for ANN-based indoor localization that mitigates RSSI variation.
  • To improve the stability and performance of deep neural network (DNN) models for indoor positioning.

Main Methods:

  • Employed wavelet scattering transform for feature extraction, ensuring stability against minor deformations and rotation invariance.
  • Utilized zero-th and first layer decomposition coefficients, concatenating scattering path coefficients as input features for a DNN model.
  • Validated the proposed algorithm using real-world indoor measurement data.

Main Results:

  • The proposed feature extraction method demonstrated stability in the face of RSSI variations.
  • The deep neural network model, using the extracted features, achieved good performance in indoor localization.
  • Experimental results confirm the effectiveness of the wavelet scattering transform for robust feature extraction in this context.

Conclusions:

  • Wavelet scattering transform offers a reliable feature extraction solution for ANN-based indoor localization systems.
  • The developed method significantly enhances the resilience of indoor localization algorithms to signal instability.
  • This approach paves the way for more accurate and dependable indoor positioning solutions.