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Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework.

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Summary
This summary is machine-generated.

This study introduces a novel multi-source joint recognition framework (MSJR) for radar mode recognition. The MSJR framework enhances recognition accuracy and robustness, even with signal defects, by embedding prior radar knowledge into machine learning models.

Keywords:
mode recognitionmulti-source feature extractionnon-specific radarresidual neural networkssupport vector machines

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Area of Science:

  • Radar Systems Engineering
  • Machine Learning Applications
  • Signal Processing

Background:

  • Radar mode recognition is crucial for interpreting multi-functional radar behavior.
  • Existing methods often require large neural networks and struggle with training-test data mismatches.
  • Signal defects pose challenges for accurate radar mode identification.

Purpose of the Study:

  • To develop an effective radar mode recognition framework for non-specific radars.
  • To address the limitations of purely data-driven approaches and improve robustness against data mismatches and signal defects.
  • To enhance the accuracy and reliability of radar behavior interpretation.

Main Methods:

  • A multi-source joint recognition framework (MSJR) combining residual neural network (ResNet) and support vector machine (SVM).
  • Embedding prior radar knowledge into the machine learning model for targeted feature learning.
  • A two-stage cascade training method to leverage ResNet's data representation and SVM's classification capabilities.
  • Combining manual intervention with automatic feature extraction.

Main Results:

  • The proposed MSJR model achieved an average recognition rate improvement of 33.7% compared to purely data-driven models.
  • Recognition rate increased by 12% compared to other state-of-the-art models (AlexNet, VGGNet, LeNet, ResNet, ConvNet).
  • MSJR maintained over 90% recognition rate with 0-35% leaky pulses in the test set, demonstrating robustness.

Conclusions:

  • The MSJR framework effectively improves radar mode recognition accuracy and robustness by integrating prior knowledge.
  • The model demonstrates superior performance and resilience, particularly under conditions of signal defects and data variability.
  • This approach offers a significant advancement in interpreting unknown radar signals with similar characteristics.