雷达信号的智能进化Deinterleaving:从基础算法到认知AI前沿的系统审查
Zhijie Qu1, Jinquan Zhang1, Yuewei Zhou1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
雷达信号脱在复杂的环境中面临着挑战. 深度学习提供了一个范式转变,人工智能的进步推动了电子智能的智能和自主脱系统.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 现代电磁环境 (CME) 对雷达信号脱构成重大挑战.
- 传统的脱方法在复杂的场景中表现出性能限制.
- 人工智能 (AI),特别是深度学习,正在彻底改变雷达信号处理.
研究的目的:
- 提供对雷达信号脱技术的全面审查.
- 将基础方法与先进的深度学习方法相结合.
- 确定当前的挑战和未来的研究方向.
主要方法:
- 系统评估经典的脱算法 (例如基于PRI的搜索,集群).
- 分析深度学习架构 (RNN,变压器,CNN,GNN) 在脱过程中.
- 探索新兴领域:自主监督学习,超级学习,多站融合和LLMs.
主要成果:
- 经典方法在复杂的CME中存在固有的局限性.
- 深度学习模型在整个架构中显示出显著的承诺和多样化的性能.
- 像LLM这样的新兴技术为增强语义推理提供了潜力.
结论:
- 对于标准化的基准和数据集存在关键需求.
- 开放性挑战包括开放式集识别,模型可解释性和实时部署.
- 未来的研究应该专注于端到端的智能和自主脱系统.
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