Abstract
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 33 modulation types across 13 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 or hybrid 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.
Abstract (translated)
长久以来，缺乏标准化的数据集阻碍了自动内脉冲调制分类（AIMC）的进展——这是一个雷达信号分析中至关重要的任务，尤其是在噪声或退化条件下。AIMC旨在从复杂的同相和正交(I/Q)表示中识别单个雷达脉冲内的调制类型，从而实现对内脉冲结构的自动化解读。本文介绍了AIMC-Spec，这是一个全面的人工合成数据集，用于基于频谱图的图像分类，涵盖了33种调制类型，并在13个信噪比(SNR)级别上进行了测试。 为了基准化AIMC-Spec，研究者重新实现了五种具有代表性的深度学习算法——从轻量级CNNs和去噪架构到基于变换器的网络，并以统一的输入格式对其进行了评估。结果表明，在不同信噪比下，频率调制（FM）信号被分类得更为可靠，尤其是在低SNR条件下。一项专门针对FM的测试进一步展示了调制类型与网络架构如何影响分类器的鲁棒性。 AIMC-Spec建立了一个可重复的基础，并为未来在自动内脉冲调制分类领域的研究和标准化工作提供了基础。
URL
https://arxiv.org/abs/2601.08265
