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Recognition of PRI modulation using an optimized convolutional neural network with a gray wolf optimization based on

Mahshid Khodabandeh¹, Azar Mahmoodzadeh², Hamed Agahi¹

¹Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

Scientific Reports

|October 1, 2025

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This study introduces a novel real-time method for identifying pulse repetition interval (PRI) modulation types in electronic warfare. The approach achieves high accuracy in noisy conditions, outperforming existing models for threat radar detection.

Area of Science:

Electronic Warfare
Radar Signal Processing
Artificial Intelligence

Background:

Accurate threat radar detection is crucial for modern electronic warfare (EW) and electronic intelligence (ELINT).
Identifying pulse repetition interval (PRI) modulation is challenging due to noise, missed pulses, and outliers in real-world scenarios.
Existing methods struggle with noisy data and computational complexity, limiting their real-time application.

Purpose of the Study:

To develop a robust, real-time approach for recognizing six common PRI modulation types in complex and noisy electronic warfare environments.
To enhance the accuracy and efficiency of PRI modulation identification for improved threat radar detection.
To address the limitations of traditional methods in handling noisy radar signals.

Main Methods:

Keywords:

Convolutional neural network Extreme learning machine Gray wolf optimizer Pulse repetition interval modulation

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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

A four-step real-time method combining Convolutional Neural Networks (CNNs) optimized by Gray Wolf Optimization (GWO) for feature extraction.
Integration of Extreme Learning Machines (ELMs) to improve model time complexity, with GWO further tuning ELM parameters.
Comparative analysis against five transfer learning-based CNN models using simulated and real-world PRI datasets.

Main Results:

The proposed method achieved 98.23% accuracy on simulated data and 99.20% on real-world data.
Training time for 42,000 images was 69.45 seconds, confirming its real-time capability.
Demonstrated superior performance and enhanced resilience to noise compared to traditional Deep Convolutional Neural Network (DCNN) models.

Conclusions:

The developed four-step approach offers an effective and efficient solution for PRI modulation recognition in challenging EW environments.
The method provides a suitable option for systems prioritizing accuracy and speed in threat radar detection.
The enhanced resilience and efficiency make it a valuable tool for electronic support measures and electronic intelligence applications.