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LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.

Do-Hyun Park1, Min-Wook Jeon1, Da-Min Shin1

  • 1Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea.

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Summary

This study introduces a novel deep learning model for detecting low-probability-of-intercept (LPI) radar signals. By utilizing the periodic autocorrelation function (PACF) and long short-term memory networks, it enhances radar signal detection performance.

Keywords:
deep learningelectronic warfarelow-probability-of-interceptsignal detectiontime-series analysis

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Area of Science:

  • Electronic Warfare
  • Signal Processing
  • Artificial Intelligence

Background:

  • Detecting low-power, low-probability-of-intercept (LPI) radar signals is challenging in electronic warfare systems.
  • Existing statistical and deep learning methods often fail due to overlooking inherent radar signal characteristics.
  • Current approaches have limitations in achieving optimal radar signal detection performance.

Purpose of the Study:

  • To develop a deep learning-based detection model for LPI radar signals.
  • To leverage the periodicity characteristic of radar signals for improved detection.
  • To enhance the performance and efficiency of radar signal detection systems.

Main Methods:

  • Utilized the periodic autocorrelation function (PACF) to capture pulse repetition characteristics in time-series data.
  • Developed a deep learning model employing long short-term memory (LSTM) networks for feature extraction and detection.
  • Extracted radar signal features from PACF for input into the neural network.

Main Results:

  • The proposed model demonstrated superior performance compared to existing deep learning models using conventional autocorrelation or spectrogram inputs.
  • The model achieved high performance even with a shallow neural network architecture due to robust feature extraction.
  • The developed model is lighter and more efficient than existing deep learning-based detection models.

Conclusions:

  • The deep learning model effectively utilizes radar signal periodicity via PACF for enhanced LPI radar detection.
  • The proposed method offers a significant advancement in electronic warfare capabilities for signal detection.
  • The model provides a computationally efficient and high-performing solution for challenging radar environments.