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Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

Zhutian Yang¹, Wei Qiu², Hongjian Sun³

¹School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. yangzhutian@hit.edu.cn.

Sensors (Basel, Switzerland)

|March 2, 2016

Summary

This study introduces a new method for radar emitter recognition using 3D features and transfer learning. The approach enhances accuracy and robustness in complex noise environments.

Keywords:

Wigner–Ville distribution radar emitter recognition relevance vector machine three-dimensional distribution feature transfer learning

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Area of Science:

Electrical Engineering
Signal Processing
Artificial Intelligence

Background:

Radar emitter signal recognition faces challenges due to increasing electromagnetic signal complexity.
Multi-component radar emitter recognition in noisy environments requires advanced techniques.

Purpose of the Study:

To propose a novel approach for radar emitter recognition in complicated noise environments.
To enhance the robustness and accuracy of radar emitter recognition against signal noise rate (SNR) variations.

Main Methods:

A three-dimensional distribution feature (cubic feature) is proposed to capture intra-pulse modulation information.
Transfer learning is employed to reconstruct features, improving robustness to SNR variations.
Relevance Vector Machine (RVM) is utilized for the classification of radar emitter signals.

Main Results:

The proposed method demonstrates superior performance in accuracy compared to existing approaches.
The approach exhibits enhanced robustness against variations in signal noise rate (SNR).
Simulations validate the effectiveness of the novel radar emitter recognition technique.

Conclusions:

The novel approach effectively addresses the challenges of radar emitter recognition in complex noise.
The combination of 3D features and transfer learning offers a robust solution for signal recognition.
This method provides a significant advancement in the field of radar signal processing and identification.