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Enhanced PRIM recognition using PRI sound and deep learning techniques.

Seyed Majid Hasani Azhdari1, Azar Mahmoodzadeh1, Mohammad Khishe2

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This study introduces a novel three-stage method using Pulse Repetition Interval (PRI) sound for enhanced radar identification. The approach effectively addresses distortions in electronic support measure (ESM) and electronic intelligence (ELINT) systems, improving accuracy.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Pulse Repetition Interval modulation (PRIM) is crucial for radar identification in Electronic Support Measure (ESM) and Electronic Intelligence (ELINT) systems.
  • Distortions like missing pulses, spurious pulses, jitters, and noise challenge accurate PRIM recognition in real-world scenarios.

Purpose of the Study:

  • To develop and evaluate a novel three-stage approach for robust PRIM recognition using PRI sound.
  • To enhance the accuracy and reliability of radar identification systems facing signal distortions.

Main Methods:

  • A three-stage method combining a transfer learning-aided Deep Convolutional Neural Network (DCNN) for feature extraction, an Extreme Learning Machine (ELM) for classification, and a Gray Wolf Optimizer (GWO) for refinement.
  • Utilized a real experimental dataset of PRI sound patterns and evaluated eight pre-trained DCNN architectures.

Main Results:

  • VGG16 and ResNet50V2 DCNNs achieved high recognition accuracies (97.53% and 96.92%, respectively).
  • Integration with ELM and GWO further optimized accuracy to 98.80% and 97.58% for specific configurations.
  • The proposed method demonstrated significant effectiveness in recognizing PRIM amidst various distortions.

Conclusions:

  • The novel three-stage approach effectively enhances PRIM recognition accuracy, particularly in challenging ESM/ELINT environments.
  • Leveraging PRI sound offers a promising avenue for improving radar identification systems against real-world signal impairments.
  • The combination of DCNN, ELM, and GWO presents a powerful strategy for advanced radar signal processing.