雷达工作模式识别通过对信号的截获分析来辨识雷达功能和行为状态,是情报侦察、电子对抗领域的重要内容。该文从适应背景的不同,研究了基于知识驱动和数据驱动算法的发展历程,从统计分析、行为推理、传统机器学习和深度学习4 个阶段总结了现有算法的基本思路、创新点和局限性,归纳了当前研究存在的难点与挑战。统计分析方法解决了信号背景清晰、简单调制类型下的识别问题。行为推理方法通过概率计算分析雷达模式的内在关联性,具备了常规模式识别的能力。传统机器学习从数据分布的角度提取深层规律,在人工配合下能够实现复杂条件下的模式识别。深度学习方法基本摆脱人工的干预,通过“端到端”的识别实现了自动化的处理。

Radar operational mode recognition is achieved through the analysis of intercepted signals to identify radar functions and behavioral states, which is an important part of electronic reconnaissance and electronic countermeasures. The development of knowledge-driven and data-driven algorithms is examined from different contextual backgrounds. The existing algorithms are summarized in four stages: statistical analysis, behavioral reasoning, traditional machine learning, and deep learning, highlighting their fundamental concepts, innovations, and limitations. Additionally, the current challenges and difficulties in the research are discussed. Statistical analysis methods effectively address recognition issues under clear signal backgrounds and simple modulation types. Behavioral reasoning methods utilize probabilistic calculations to analyze the intrinsic relationships among radar modes, demonstrating capabilities in conventional pattern recognition. Traditional machine learning extracts deeper patterns from data distributions, enabling pattern recognition under complex conditions with human assistance. In contrast, deep learning methods largely eliminate human intervention, achieving automated processing through end-to-end recognition.

TN971