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Updated: Jul 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference.

Xiaojun Wu1,2, Yibo Zhou1,2, Daolong Wu3

  • 1School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|September 28, 2023
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Summary
This summary is machine-generated.

A new model, AFUCR-SNRSN, enhances flying ad-hoc network (FANET) interference recognition in noisy battlefield environments. It achieves high accuracy for known and unknown signals, even at low jamming-to-noise ratios.

Keywords:
OCSVMSNCScommunication interferencenew class rejectionsoft threshold

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Flying ad-hoc networks (FANETs) in complex battlefields struggle with manual feature extraction, low recognition rates in noise, and identifying unknown interference signals.
  • Existing methods lack robustness in challenging signal environments, hindering effective communication management and security.

Purpose of the Study:

  • To develop an advanced model for accurate recognition of communication interference signals in FANETs, addressing limitations in noisy conditions and unknown signal types.
  • To improve the robustness and accuracy of interference signal identification within FANETs for enhanced operational effectiveness.

Main Methods:

  • Introduction of a simple non-local correction shrinkage (SNCS) module with adaptive thresholding and local importance-based pooling (LIP) to enhance signal features and reduce noise.
  • Development of a joint loss function combining cross-entropy and center loss for effective model training.
  • Proposal of an acceptance factor for unknown class recognition, integrated into the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model.

Main Results:

  • The AFUCR-SNRSN model demonstrated superior recognition accuracy compared to other methods, especially in low jamming-to-noise ratio (JNR) scenarios.
  • Accuracy for known interference signals increased by 4-9%, reaching 99% at -6 dB JNR.
  • The false positive rate (FPR) for recognizing unknown interference signals was significantly reduced to 9%.

Conclusions:

  • The AFUCR-SNRSN model effectively addresses the challenges of interference signal recognition in FANETs, particularly under adverse conditions.
  • The proposed model offers a robust solution for both known and unknown interference signal identification, enhancing FANET security and reliability.
  • The adaptive thresholding, feature enhancement, and joint loss function contribute to the model's high performance in complex electromagnetic environments.