---
license: other
tags:
  - vision-transformer
  - radar
  - signal-processing
  - electronic-warfare
  - 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.

**Training.** Fine-tuned with SGD (lr=0.001) and sparse categorical
cross-entropy loss on top of GC-ViT's pretrained backbone.

**Evaluation protocol.** Tested across a wide SNR sweep (roughly −14 dB to
+8 dB in 2 dB steps) with held-out test sets per SNR level, so performance
is reported as a function of noise level rather than a single aggregate
number — directly relevant to EW receivers that must operate across
unknown, often very low, intercept SNRs.

## Results
- ~80% recognition accuracy at −12 dB SNR, substantially outperforming prior
  methods at that noise level
- Performance degrades gracefully across the full tested SNR sweep rather
  than collapsing sharply, consistent with the global self-attention
  mechanism recovering structure that local-only (CNN) models miss at low SNR
- Demonstrates robustness for EW situational-awareness applications in
  complex electromagnetic environments

## Code
`code/Global_Context_ViT_for_RadarSiganls.ipynb` — the training/evaluation
notebook used for this paper. It's Colab-specific (mounts Google Drive and
loads a locally-trained `.h5` weights file 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. Training data:
see the Kaggle dataset link above.

**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](https://doi.org/10.1088/2631-8695/ad8b96)
(subscription required — not open access, so no PDF is included here; link only)

## Citation
```bibtex
@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}
}