SidraBhatti/gc-vit-autonomous-radar-modulation-recognition

GC-ViT: AI-Based Global Context Vision Transformer for Radar Signal Modulation Recognition

An AI-based Global Context Vision Transformer (GC-ViT) that leverages the short-time Fourier transform (STFT) phase spectrum for feature extraction to identify phase-coded radar waveforms — a key capability for electronic warfare (EW) systems facing the growing use of low-probability-of-intercept (LPI) radars. Combines local and global self-attention to improve recognition robustness at low SNR.

Dataset: (https://www.kaggle.com/datasets/sidrabhatti/latestdataset-cnn)

Role & Attribution

Sidra Ghayour Bhatti — first author; conceived, implemented, and evaluated the GC-ViT model. Co-authored with Mohsin Ullah (Dept. of Electrical Engineering, Capital University of Science and Technology, Islamabad).

Method

Input representation. Each intercepted radar pulse is converted to its short-time Fourier transform (STFT) phase spectrum rather than the more commonly used magnitude spectrum — the deliberate choice is because phase-coded waveforms (Barker codes, P1–P4 polyphase codes, etc.) are defined by their intrapulse phase-modulation pattern, not by how their energy is distributed across frequency. Two different phase codes can have near-identical magnitude spectra while differing sharply in phase, so a magnitude-only spectrogram throws away the very information that distinguishes the waveform classes; the phase spectrum keeps it. The resulting 2D time-frequency phase map is cropped to its informative region and resized to 224×224×3, turning a 1D IQ waveform recognition problem into a 2D image classification problem that a vision architecture can exploit.

Model. A GC-ViT (Global Context Vision Transformer, Tiny variant) backbone, pretrained then fine-tuned with a 6-class softmax classification head for phase-coded waveform families. GC-ViT combines local window-based self-attention (fine-grained spectrogram texture) with a global query token (long-range structure across the full time-frequency map) in each block — the architectural reason it outperforms CNN baselines at low SNR, where discriminative structure is spread across the spectrogram rather than localized.

Figure 3: The suggested GC-ViT architecture — an input spectrogram passes through a stem, then alternating stages of local + global multi-head self-attention (MSA) blocks with a global token generator feeding each stage, downsampling and doubling channel depth between stages, ending in 2D average pooling and a classification head.

Training hyperparameters.

Parameter	Value
Learning rate	5e-4 (cosine scheduler, decay every 30 epochs)
Input resolution	224×224×3
Attention heads (Stage 1–4)	3, 6, 12, 24
Window size (Stage 1–4)	7, 7, 14, 7
Epochs	200
MLP ratio	2
Drop path rate	0
Layer scaling coefficient	1e-5
Activation	GELU
Attention block type	Window Attention + Global
Spatial dimension reduction	2× per stage

Results

Recognition accuracy (%) vs. SNR, per phase-coded waveform type:

Modulation	−12 dB	−8 dB	−4 dB	0 dB	4 dB	8 dB
Barker	58.3	95.8	100	100	100	100
Frank	80.0	83.3	100	100	100	100
P1	90.0	91.6	100	100	100	100
P2	100	100	100	100	100	100
P3	77.7	94.4	100	100	100	100
P4	66.67	94.4	100	100	100	100

All six waveform types reach 100% accuracy by −4 dB SNR; Barker and P4 codes are the weakest at −12 dB (most easily confused with other schemes at extreme noise), while P2 codes are correctly recognized at every tested SNR level. Comparable to or exceeding other transformer-based LPI radar recognition methods in the literature at similar SNR ranges.

Code

Global_Context_ViT_for_RadarSiganls.ipynb — the training/evaluation notebook used for this paper. It's Colab-specific (mounts Google Drive, expects a zipped private dataset and a locally-trained .h5 weights file that are not included here), so it won't run standalone, but it documents the exact preprocessing, model setup, and SNR-sweep evaluation protocol behind the reported results.

Published: Bhatti, S.G. & Ullah, M. (2024). "Radar signal modulation identification using global context vision transformer." Engineering Research Express, 6(4), 045331. doi.org/10.1088/2631-8695/ad8b96 (subscription required — not open access, so no PDF is included here; link only)

Citation

@article{bhatti2024radar,
  author  = {Bhatti, Sidra Ghayour and Ullah, Mohsin},
  title   = {Radar signal modulation identification using global context vision transformer},
  journal = {Engineering Research Express},
  volume  = {6},
  number  = {4},
  pages   = {045331},
  year    = {2024},
  doi     = {10.1088/2631-8695/ad8b96}
}