AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions

TL;DR

AIMC-Spec introduces a synthetic dataset for evaluating deep learning models on automatic intrapulse modulation classification across various noise levels, addressing the lack of standard benchmarks in radar signal analysis.

Contribution

This paper presents AIMC-Spec, a new comprehensive dataset for AIMC, and benchmarks multiple deep learning models, highlighting their performance variations under different noise conditions.

Findings

FM signals are classified more reliably than PM signals at low SNRs

Deep learning model performance varies significantly across architectures

A focused FM-only test shows the impact of modulation type and architecture on robustness

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.