GNSS Interference Identification Driven by Eye Pattern Features: ICOA-CNN-ResNet-BiLSTM Optimized Deep Learning Architecture

Chuanyu Wu ^1,2, Yuanfa Ji ^1,3, Xiyan Sun ^1,4

⁰Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China.

Entropy (basel, Switzerland) +

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September 27, 2025

View abstract on PubMed

Summary

This study introduces an AI framework using eye diagrams to classify Global Navigation Satellite System (GNSS) interference, enhancing security. The novel approach achieves high accuracy in identifying various jamming types.

Area Of Science

Signal Processing
Artificial Intelligence
Cybersecurity

Background

Global Navigation Satellite Systems (GNSSs) face significant security challenges from signal interference.
Existing methods for interference detection and classification often lack efficiency and accuracy.

Purpose Of The Study

To propose a novel deep learning framework for intelligent classification of GNSS interference types.
To enhance the security and reliability of GNSS operations through advanced signal analysis.

Main Methods

Transforming GNSS signals into 2D eye diagrams for visual representation.
Utilizing entropy-centric feature analysis for interference discrimination.
Designing a hybrid deep learning architecture (CNN, ResNet, BiLSTM) for signal analysis.
Employing an improved coati optimization algorithm (ICOA) for hyperparameter tuning.

Main Results

The proposed method achieved high performance metrics: 98.02% accuracy, 97.09% precision, 97.24% recall, 97.14% F1 score, and 99.65% specificity.
Demonstrated significant improvements over existing models on diverse jamming datasets.
The ICOA algorithm showed over 30% improvement in convergence accuracy compared to mainstream methods.

Conclusions

The developed eye diagram-based deep learning framework offers an efficient and accurate solution for GNSS interference identification.
The entropy-aware feature extraction and hybrid network architecture are key to the method's success.
This research provides a practically feasible approach to bolster GNSS security against various interference threats.

Keywords:

GNSS interference detection deep learning entropy image processing metaheuristic optimization signal classification