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The Intelligent Evolution of Radar Signal Deinterleaving: A Systematic Review from Foundational Algorithms to

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Modern electromagnetic environments (CME) present significant challenges to radar signal deinterleaving.
  • Traditional deinterleaving methods exhibit performance limitations in complex scenarios.
  • Artificial intelligence (AI), especially deep learning, is revolutionizing radar signal processing.

Purpose of the Study:

  • To provide a comprehensive review of radar signal deinterleaving techniques.
  • To bridge foundational methods with advanced deep learning approaches.
  • To identify current challenges and future research directions.

Main Methods:

  • Systematic evaluation of classical deinterleaving algorithms (e.g., PRI-based search, clustering).
  • Analysis of deep learning architectures (RNNs, Transformers, CNNs, GNNs) in deinterleaving.
  • Exploration of emerging areas: self-supervised learning, meta-learning, multi-station fusion, and LLMs.

Main Results:

  • Classical methods have inherent limitations in complex CMEs.
  • Deep learning models show significant promise and varied performance across architectures.
  • Emerging techniques like LLMs offer potential for enhanced semantic reasoning.

Conclusions:

  • A critical need exists for standardized benchmarks and datasets.
  • Open challenges include open-set recognition, model interpretability, and real-time deployment.
  • Future research should focus on end-to-end intelligent and autonomous deinterleaving systems.