构建首个变噪条件下的雷达脉内调制分类基准数据集
AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions
- 合成30类调制信号，覆盖5个信噪比等级
- 频率调制识别率显著高于相位调制，尤其在低信噪比下
- 适合雷达信号分析、电子战系统研发人员参考
自动脉内调制分类（AIMC）是电子支援系统中雷达信号分析的关键任务，尤其在噪声或信号退化条件下。该任务旨在从单个雷达脉冲的复数同相/正交（I/Q）表示中识别调制类型，实现脉内结构的自动化解析。本文提出AIMC-Spec，一个面向光谱图图像分类的综合性合成数据集，包含30种调制类型和5个信噪比（SNR）水平。为评估该数据集，五种代表性深度学习模型——从轻量级CNN、去噪架构到基于Transformer的网络——被统一输入格式重新实现并测试。结果表明性能差异显著：频率调制（FM）信号识别更可靠，尤其在低信噪比下；针对仅含FM信号的专项测试进一步揭示了调制类型与网络架构对分类器鲁棒性的影响。AIMC-Spec建立了可复现的基准，为该领域未来研究与标准化提供基础。
原文摘要 · Abstract (English)
A lack of standardized datasets has long hindered progress in automatic intrapulse modulation classification (AIMC), a critical task in radar signal analysis for electronic support systems, particularly under noisy or degraded conditions. AIMC seeks to identify the modulation type embedded within a single radar pulse from its complex in-phase and quadrature (I/Q) representation, enabling automated interpretation of intrapulse structure. This paper introduces AIMC-Spec, a comprehensive synthetic dataset for spectrogram-based image classification, encompassing 30 modulation types across 5 signal-to-noise ratio (SNR) levels. To benchmark AIMC-Spec, five representative deep learning algorithms ranging from lightweight CNNs and denoising architectures to transformer-based networks were re-implemented and evaluated under a unified input format. The results reveal significant performance variation, with frequency-modulated (FM) signals classified more reliably than phase-modulated (PM) types, particularly at low SNRs. A focused FM-only test further highlights how modulation type and network architecture influence classifier robustness. AIMC-Spec establishes a reproducible baseline and provides a foundation for future research and standardization in the AIMC domain.
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