AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions
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 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.
Cite
@article{arxiv.2601.08265,
title = {AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions},
author = {Sebastian L. Cocks and Salvador Dreo and Brian Ng and Feras Dayoub},
journal= {arXiv preprint arXiv:2601.08265},
year = {2026}
}
Comments
This version updates the previously released dataset by reducing storage requirements, revising the SNR calculation procedure, and restructuring the dataset format The first version of this work was published in IEEE Access DOI: 10.1109/ACCESS.2025.3645091