AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions
This addresses a critical problem for electronic support systems in radar analysis by providing a reproducible benchmark, though it is incremental as it focuses on dataset creation and baseline evaluation rather than novel method development.
The paper tackles the lack of standardized datasets for automatic intrapulse modulation classification (AIMC) in radar signal analysis by introducing AIMC-Spec, a comprehensive synthetic dataset with 33 modulation types across 13 SNR levels, and benchmarks it with five deep learning algorithms, revealing significant performance variation with FM signals classified more reliably than other types at low SNRs.
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.