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Related Experiment Video

Updated: May 14, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Domain-Adversarial Neural Network for UWB NLOS Identification in Multiple Environments.

Suying Jiang1,2, Jiachun Li3, Yadong Xu1

  • 1School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces a new method for identifying Non-Line-of-Sight (NLOS) signals in Ultra-Wideband (UWB) localization systems. The approach enhances accuracy and generalization across different environments, improving positioning performance.

Area of Science:

  • Signal Processing
  • Machine Learning
  • Localization Systems

Background:

  • Accurate Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) identification is vital for Ultra-Wideband (UWB) localization systems.
  • Existing NLOS identification methods lack cross-domain adaptability and fail to generalize to new environments.
  • Traditional feature extraction methods struggle with the nonlinear characteristics of Channel Impulse Response (CIR) data.

Purpose of the Study:

  • To propose a novel NLOS identification strategy with enhanced cross-domain generalization capabilities for UWB systems.
  • To develop a robust feature extraction model capable of capturing deep nonlinear characteristics from CIR data.
  • To integrate a hybrid feature extraction model with a Domain-Adversarial Neural Network (DANN) for improved NLOS identification.

Main Methods:

Keywords:
Domain-Adversarial Neural Network (DANN)NLOS identificationUltra-Wideband (UWB)cross-domain

Related Experiment Videos

Last Updated: May 14, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • A CNN-DAE-MLP-Attention (CDM) hybrid model was developed for high-quality channel feature extraction from raw CIR data and handcrafted features.
  • The CDM model was integrated into the DANN framework, creating the CDMD algorithm for robust feature representation and domain adaptation.
  • The CDMD algorithm was evaluated using measured data from diverse real-world scenarios.

Main Results:

  • The proposed CDMD algorithm demonstrated strong generalization ability in cross-domain NLOS identification.
  • Accuracies of 77.00% and 72.84% were achieved for cross-domain NLOS recognition from underground parking garage to corridor and underground parking garage to lobby, respectively.
  • The study confirmed that limited target-domain samples are sufficient for accurate cross-domain transfer using the proposed model.

Conclusions:

  • The CDMD algorithm significantly enhances cross-domain NLOS identification performance in UWB localization systems.
  • The hybrid feature extraction and domain-adversarial approach overcomes limitations of traditional methods.
  • The proposed strategy offers a promising solution for robust and adaptable NLOS identification in complex environments.