Create README.md

README.md

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+---
+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}
+}