Upload README.md

README.md

@@ -1,150 +1,392 @@
+<div align="center">
+
+<img src="assets/banner.jpg" alt="Radar Micro-Doppler AI Banner" width="100%"/>
+
+<br/>
+
+[![CI](https://github.com/bukac82/radar-microdoppler-ai/actions/workflows/ci.yml/badge.svg)](https://github.com/bukac82/radar-microdoppler-ai/actions)
+[![Python](https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12-blue?logo=python)](https://www.python.org/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+[![PennyLane](https://img.shields.io/badge/quantum-PennyLane-blueviolet?logo=data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGNpcmNsZSBjeD0iOCIgY3k9IjgiIHI9IjgiIGZpbGw9IiM3QzNBRUQiLz48L3N2Zz4=)](https://pennylane.ai/)
+[![PyTorch](https://img.shields.io/badge/deep_learning-PyTorch-EE4C2C?logo=pytorch)](https://pytorch.org/)
+[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
+[![HuggingFace Dataset](https://img.shields.io/badge/🤗%20Dataset-MicroDopplerSignatures-orange)](https://huggingface.co/datasets/bukac82/MicroDopplerSignatures)
+
+</div>
+
+---
+
+## 🛰️ What Is This?
+
+**Radar Micro-Doppler AI** is a research-grade, end-to-end pipeline for classifying helicopter and multi-rotor drone types from their **micro-Doppler radar signatures** — the unique frequency modulation pattern created by rotating rotor blades.
+
+The repository brings together **three paradigms of AI** on a single, unified dataset:
+
+| Paradigm | Methods | Input |
+|----------|---------|-------|
+| **Classical ML** | SVM · Random Forest · XGBoost · k-NN | 14 hand-crafted physics features |
+| **Quantum ML** | Quantum Kernel SVM · VQC (PennyLane) | 4-D PCA → angle embedding |
+| **Deep Learning** | ResNet CNN · Bi-LSTM · Spectrogram Transformer | STFT spectrograms / raw IQ |
+
+Plus:
+- 🔒 **Robustness Testing** — SNR sweeps (−5 to 30 dB), FGSM & PGD adversarial attacks
+- 🔍 **Open-Set Recognition** — Energy-based OOD detection for unknown aerial targets
+- 💡 **Explainability** — SHAP feature importance + Grad-CAM heatmaps
+
+---
+
+## 📊 Benchmark Results
+
+> Results on the held-out test set (20% of 5,000 samples), SNR ∈ [5, 25] dB.
+
+| Model | Accuracy | Macro-F1 | ROC-AUC |
+|-------|----------|----------|---------|
+| SVM (RBF, C=10) | 0.9620 | 0.9619 | 0.9971 |
+| Random Forest (n=200) | 0.9780 | 0.9780 | 0.9994 |
+| XGBoost | 0.9740 | 0.9739 | 0.9991 |
+| k-NN (k=7) | 0.9480 | 0.9479 | 0.9940 |
+| **CNN (ResNet-style)** | **0.9870** | **0.9870** | **0.9998** |
+| Bi-LSTM | 0.9810 | 0.9810 | 0.9996 |
+| Spectrogram Transformer | 0.9830 | 0.9829 | 0.9997 |
+| Quantum Kernel SVM (4q) | 0.8200 | 0.8192 | 0.9421 |
+| VQC (4q, 2 layers) | 0.7867 | 0.7851 | 0.9108 |
+
+> **Key insight**: The blade flash rate (`n_blades × RPM / 60`) is the single most discriminative feature, directly readable from the STFT spectrogram as periodic vertical striations.
+
 ---
-license: mit
-language:
-- en
-tags:
-- radar
-- signal-processing
-- micro-doppler
-- helicopter
-- classification
-- synthetic
-- iq-data
-- time-series
-pretty_name: Micro-Doppler Signatures (Helicopter)
-size_categories:
-- 100K<n<1M
-task_categories:
-- tabular-classification
-- audio-classification
+
+## 🎯 Target Classes
+
+### 🚁 Helicopters
+
+| Class | Helicopter | Blades | RPM | Blade Radius |
+|-------|-----------|--------|-----|-------------|
+| **2-blade** | Bell UH-1 (Huey) | 2 | 300–350 | 6.5–7.5 m |
+| **3-blade** | Aérospatiale Gazelle | 3 | 360–400 | 4.5–5.5 m |
+| **4-blade** | Apache AH-64 / UH-60 Black Hawk | 4 | 250–300 | 7.0–8.5 m |
+
+### 🛸 Drones (UAVs)
+
+| Class | Type | Rotors | Blades/Rotor | RPM | Blade Radius | Reference |
+|-------|------|--------|--------------|-----|--------------|-----------|
+| **quadcopter** | Quadcopter | 4 | 2 | 4,000–8,000 | 7.5–12.5 cm | DJI Phantom / Mavic |
+| **hexacopter** | Hexacopter | 6 | 2 | 2,000–4,000 | 20–35 cm | DJI Matrice 600 |
+| **octocopter** | Octocopter | 8 | 2 | 1,500–3,000 | 25–45 cm | DJI S1000 |
+
 ---
 
-# Micro-Doppler Signatures - Helicopter Classification Dataset
+## 🗂️ Project Structure
 
-Synthetic IQ-sampled radar returns from three helicopter types, generated using a physics-based sinc micro-Doppler scattering model. Designed for benchmarking ML classifiers on rotating-blade target identification.
+```
+radar-microdoppler-ai/
+│
+├── 📓 notebooks/
+│   ├── 01_EDA_and_Signal_Analysis.ipynb
+│   ├── 02_Classical_ML_Benchmark.ipynb
+│   ├── 03_Deep_Learning.ipynb
+│   └── 04_Quantum_ML.ipynb
+│
+├── 📡 Dataset/                  ← Data generation & loading
+├── 🔧 Preprocessing/            ← STFT spectrograms, feature extraction
+├── 🤖 Classical_ML/             ← SVM, RF, XGBoost, k-NN
+├── ⚛️  Quantum_ML/              ← QK-SVM, VQC (PennyLane)
+├── 🧠 Deep_Learning/            ← CNN, LSTM, Transformer (PyTorch)
+├── 🛡️  Robustness_Testing/      ← SNR sweep, FGSM/PGD attacks
+├── 🔓 Open_Set_Recognition/     ← Energy OOD, AUROC/OSCR metrics
+├── 💡 Explainability/           ← SHAP, Grad-CAM
+├── 📈 Evaluation/               ← Unified benchmark & metrics
+│
+├── 🧪 tests/                    ← Pytest unit tests
+├── 📜 scripts/
+│   ├── download_data.py         ← Dataset download / local generation
+│   └── run_full_pipeline.py     ← End-to-end pipeline runner
+│
+├── pyproject.toml
+├── requirements.txt
+└── CITATION.cff
+```
 
-## Dataset Description
+---
 
-### What's in it
+## ⚡ Quick Start
 
-Two CSV files, each with 100,000 samples:
+### 1. Install
 
-| File | Contents |
-|------|----------|
-| `helicopter_microdoppler_dataset.csv` | Baseline - fixed radar geometry, SNR in [5, 25] dB |
-| `helicopter_microdoppler_extended_dataset.csv` | Extended - variable radar frequency (8-12 GHz), elevation angle (0-45 deg), bulk target velocity (+/-50 m/s) |
+```bash
+git clone https://github.com/bukac82/radar-microdoppler-ai.git
+cd radar-microdoppler-ai
 
-Each row is one 0.5-second radar observation window at 1 kHz sampling rate (500 complex IQ samples per window), flattened as real and imaginary columns.
+pip install -e ".[all]"          # everything: DL + QML + explainability
+# or
+pip install -e ".[dl]"           # PyTorch only
+pip install -e ".[qml]"          # PennyLane only
+```
+
+### 2. Download the Dataset
 
-### Target classes
+```bash
+# Option A — HuggingFace Hub (recommended, ~8 GB total for all 4 datasets)
+pip install huggingface-hub
+python scripts/download_data.py
 
-| Label | Helicopter | Blades | Rotor RPM | Blade length |
-|-------|-----------|--------|-----------|--------------|
-| `2` | Bell UH-1 Iroquois (Huey) | 2 | 300-350 | 6.5-7.5 m |
-| `3` | Aerospatiale Gazelle | 3 | 360-400 | 4.5-5.5 m |
-| `4` | Boeing AH-64 Apache / UH-60 Black Hawk | 4 | 250-300 | 7.0-8.5 m |
+# Option B — Direct CLI
+huggingface-cli download bukac82/MicroDopplerSignatures \
+    --repo-type dataset --local-dir .
 
-### Signal model
+# Option C — Generate locally
+python scripts/download_data.py --source generate
+```
 
-The micro-Doppler return from the k-th blade is modelled as:
+### 3. Run the Full Pipeline
+
+```bash
+python scripts/run_full_pipeline.py --nrows 5000
+```
+
+### 4. Explore Notebooks
+
+```bash
+jupyter notebook notebooks/
+```
+
+---
+
+## 📓 Notebooks
+
+| # | Notebook | Description |
+|---|----------|-------------|
+| 01 | [EDA & Signal Analysis](notebooks/01_EDA_and_Signal_Analysis.ipynb) | Dataset statistics, IQ signals, spectrograms, feature correlation |
+| 02 | [Classical ML Benchmark](notebooks/02_Classical_ML_Benchmark.ipynb) | SVM / RF / k-NN training, confusion matrices, SHAP, SNR sweep |
+| 03 | [Deep Learning](notebooks/03_Deep_Learning.ipynb) | CNN/LSTM/Transformer training curves, Grad-CAM visualization |
+| 04 | [Quantum ML](notebooks/04_Quantum_ML.ipynb) | QK-SVM & VQC on 4-D PCA features, Classical vs Quantum comparison |
+
+---
+
+## 🔬 Dataset
+
+> 🤗 **[bukac82/MicroDopplerSignatures](https://huggingface.co/datasets/bukac82/MicroDopplerSignatures)** — publicly available on HuggingFace Hub
+
+Two physics-accurate synthetic datasets generated from the **sinc-based micro-Doppler model**:
+
+| Dataset | Samples | Features | Size |
+|---------|---------|----------|------|
+| Base (`helicopter_microdoppler_dataset.csv`) | 100,000 | IQ @ 1 kHz, 0.5s window | ~2 GB |
+| Extended (`helicopter_microdoppler_extended_dataset.csv`) | 100,000 | + varied radar freq (8–12 GHz), elevation (0–45°), bulk velocity (±50 m/s) | ~2 GB |
+
+**Signal model** (Doppler from rotating blade):
 
 $$s_k(t) = L \cdot \text{sinc}\!\left(\frac{2L}{\lambda}\cos\phi_k\cos\beta\right) \exp\!\left(j\frac{4\pi L}{\lambda}\cos\phi_k\cos\beta\right)$$
 
-where phi_k(t) = omega*t + theta_0 + 2*pi*k/N_b is the instantaneous blade phase, L is blade half-length, lambda is radar wavelength, and beta is the elevation angle. AWGN is added to reach the target SNR.
-
-## Column Schema
-
-```
-label          - int {2, 3, 4}         helicopter class (number of main rotor blades)
-snr_db         - float                 signal-to-noise ratio of this sample
-n_blades       - int                   number of rotor blades
-rpm            - float                 rotor revolutions per minute
-blade_length_m - float                 blade half-length in metres
-I_0 ... I_499  - float                 in-phase (real) IQ samples
-Q_0 ... Q_499  - float                 quadrature (imaginary) IQ samples
-```
-
-Extended dataset additionally includes:
-```
-radar_freq_ghz - float                 radar carrier frequency (8-12 GHz)
-elevation_deg  - float                 target elevation angle (0-45 deg)
-velocity_ms    - float                 bulk target radial velocity (-50 to +50 m/s)
-```
-
-## Intended Use
-
-- Benchmarking classical and deep learning classifiers on radar micro-Doppler data
-- - Evaluating robustness to noise (SNR sweep experiments)
-  - - Research into quantum kernel methods on signal classification tasks
-    - - Open-set recognition and out-of-distribution detection studies
-     
-      - ## Limitations
-     
-    - - Synthetic data only - real radar returns include ground clutter, multipath, and hardware-specific artefacts not modelled here
-      - - Three helicopter classes only - does not cover fixed-wing aircraft, drones, or birds
-        - - Monostatic radar geometry assumed
-         
-          - ## Related Repository
-         
-        - Code, notebooks, and full experimental pipeline:
-        - **[bukac82/radar-microdoppler-ai](https://github.com/bukac82/radar-microdoppler-ai)**
-      - ## Citation
-    - If you use this dataset in your research, please cite the dataset/software repository and/or the relevant papers below.
-  - ### Dataset & Software Repository
-  - ```bibtex
-    @software{agnihotri2026microdoppler,
-      author    = {Agnihotri, Vikas},
-      title     = {Radar Micro-Doppler AI: End-to-End Helicopter Classification},
-      year      = {2026},
-      url       = {https://github.com/bukac82/radar-microdoppler-ai}
-    }
-    ```
-
-    ### Related Papers
-
-    **Quantum ML on NISQ Hardware:**
-    ```bibtex
-    @article{agnihotri2026quantum,
-      author    = {Agnihotri, Vikas and Kaur, Jasleen and Kaushik, Sarvagya},
-      title     = {Practical Evaluation of Quantum Kernel Methods for Radar
-                   Micro-Doppler Classification on Noisy Intermediate-Scale
-                   Quantum ({NISQ}) Hardware},
-      journal   = {arXiv preprint},
-      volume    = {arXiv:2601.22194},
-      year      = {2026},
-      url       = {https://arxiv.org/abs/2601.22194}
-    }
-    ```
-
-    **Radar-Based ATR Framework (foundational SVM/signal model):**
-    ```bibtex
-    @article{agnihotri2020radar,
-      author    = {Agnihotri, Vikas and Sabharwal, Munish},
-      title     = {An Automatic Radar Based Aerial Target Recognition Framework},
-      journal   = {Journal of Interdisciplinary Mathematics},
-      volume    = {23},
-      number    = {2},
-      pages     = {321--333},
-      year      = {2020},
-      doi       = {10.1080/09720502.2020.1737377},
-      url       = {https://doi.org/10.1080/09720502.2020.1737377}
-    }
-    ```
-
-    **Frequency Effects on Micro-Doppler (underpins extended dataset design):**
-    ```bibtex
-    @inproceedings{agnihotri2019frequency,
-      author    = {Agnihotri, Vikas and Sabharwal, Munish and Goyal, Vinay},
-      title     = {Effect of Frequency on Micro-Doppler Signatures of a Helicopter},
-      booktitle = {2019 International Conference on Advances in Big Data,
-                   Computing and Data Communication Systems (icABCD)},
-      year      = {2019},
-      doi       = {10.1109/ICABCD.2019.8851024},
-      url       = {https://doi.org/10.1109/ICABCD.2019.8851024}
-    }
-    ```
-
-    ## License
-
-    MIT - free to use for research and commercial purposes with attribution.
-    
+where $\phi_k = \omega t + \theta_0 + \frac{2\pi k}{N_b}$ is the phase of the $k$-th blade, $L$ is the blade length, $\lambda$ is the radar wavelength, and $\beta$ is the elevation angle.
+
+```python
+# Load directly in Python via HuggingFace datasets library
+from datasets import load_dataset
+
+ds = load_dataset("bukac82/MicroDopplerSignatures", split="train")
+```
+
+---
+
+## 🏗️ Architecture Overview
+
+```
+                        ┌──────────────────────────────────────────┐
+                        │           Raw IQ Signal (500 × complex)  │
+                        └─────────────────┬────────────────────────┘
+                                          │
+              ┌───────────────────────────┼────────────────────────────┐
+              ▼                           ▼                            ▼
+    ┌──────────────────┐      ┌────────────────────┐      ┌──────────────────────┐
+    │  Feature Extract │      │  STFT Spectrogram  │      │  Raw IQ (I,Q) → (T,2)│
+    │  (14-D vector)   │      │  (33 × 15 image)   │      │  for LSTM            │
+    └────────┬─────────┘      └────────┬───────────┘      └──────────┬───────────┘
+             │                         │                              │
+    ┌────────▼────────┐      ┌─────────▼──────────┐      ┌──────────▼───────────┐
+    │ Classical ML    │      │  CNN / Transformer  │      │   Bi-LSTM            │
+    │ SVM · RF · kNN  │      │  (Deep Learning)    │      │   (128 hidden)       │
+    └────────┬────────┘      └─────────┬──────────┘      └──────────┬───────────┘
+             │                         │                              │
+             └────────────────┬────────┘──────────────────────────────┘
+                              ▼
+                    ┌──────────────────┐
+                    │  Evaluation      │
+                    │  Robustness Test │
+                    │  OSR Detection   │
+                    │  Explainability  │
+                    └──────────────────┘
+```
+
+---
+
+## ⚛️ Quantum ML
+
+The quantum pipeline reduces features to 4 dimensions via PCA, then:
+
+- **QK-SVM**: Encodes features into quantum states via angle embedding; kernel computed as fidelity `|⟨φ(x)|φ(x')⟩|²`
+- **VQC**: Parameterized `StronglyEntanglingLayers` trained with Adam optimizer and cross-entropy loss
+
+```python
+from Quantum_ML.qkernel_svm import QuantumKernelSVM
+
+qksvm = QuantumKernelSVM(n_qubits=4, C=1.0)
+qksvm.fit(X_train_scaled, y_train)
+print(f"Quantum Kernel SVM accuracy: {qksvm.score(X_test_scaled, y_test):.4f}")
+```
+
+---
+
+## 🛡️ Robustness Testing
+
+```python
+from Robustness_Testing.noise_robustness import snr_sweep
+
+# Sweep from -5 dB to 30 dB
+results = snr_sweep(predict_fn, X_iq_test, y_test, snr_range=range(-5, 31, 5))
+# {-5: 0.61, 0: 0.79, 5: 0.91, 10: 0.97, 15: 0.98, ...}
+```
+
+```python
+from Robustness_Testing.adversarial import evaluate_adversarial
+
+# FGSM attack at varying epsilon
+adv_results = evaluate_adversarial(cnn_model, X_test, y_test,
+                                    epsilon_values=[0.001, 0.01, 0.05])
+```
+
+---
+
+## 💡 Explainability
+
+**SHAP** (Classical ML):
+```python
+from Explainability.shap_explainer import explain_with_shap, plot_shap_summary
+shap_values = explain_with_shap(rf_model, X_train, X_test, model_type="tree")
+plot_shap_summary(shap_values, X_test, model_name="random_forest")
+```
+
+**Grad-CAM** (CNN):
+```python
+from Explainability.gradcam import GradCAM
+cam = GradCAM(cnn_model, target_layer=cnn_model.layer3)
+heatmap = cam(spectrogram_tensor, class_idx=1)  # highlights 3-blade freq bands
+```
+
+---
+
+## 🔓 Open-Set Recognition
+
+Detects unknown aerial targets (drones, birds) not seen during training:
+
+```python
+from Open_Set_Recognition.energy_ood import EnergyOODDetector
+from Open_Set_Recognition.evaluate_osr import print_osr_report
+
+detector = EnergyOODDetector(cnn_model, threshold=-25.0)
+is_unknown = detector.predict(X_unknown)   # True = reject as unknown
+
+print_osr_report(id_energy_scores, ood_energy_scores)
+# AUROC: 0.934  |  FPR@95TPR: 0.127
+```
+
+---
+
+## 🤝 Contributing
+
+Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
+
+Ideas for contribution:
+- 🚁 Add new helicopter classes (Chinook CH-47, V-22 Osprey)
+- 🐦 Add drone / bird / UAV signatures for open-set testing
+- 📡 Add Ka-band / W-band radar parameters
+- 🔬 Implement Integrated Gradients or LIME explainability
+- ⚛️ Implement Quantum Neural Networks with larger qubit counts
+
+---
+
+## 📖 Citation
+
+If you use this project or dataset in your research, please cite the software repository and/or the relevant papers below:
+
+### Software Repository
+
+```bibtex
+@software{agnihotri2026microdoppler,
+  author    = {Agnihotri, Vikas},
+  title     = {Radar Micro-Doppler AI: End-to-End Helicopter Classification
+               with Classical, Quantum, and Deep Learning},
+  year      = {2026},
+  url       = {https://github.com/bukac82/radar-microdoppler-ai},
+  license   = {MIT}
+}
+```
+
+### Related Papers
+
+This repository builds upon the following peer-reviewed publications by the author:
+
+**Quantum ML on NISQ Hardware** (most relevant to the `Quantum_ML/` module):
+```bibtex
+@article{agnihotri2026quantum,
+  author    = {Agnihotri, Vikas and Kaur, Jasleen and Kaushik, Sarvagya},
+  title     = {Practical Evaluation of Quantum Kernel Methods for Radar
+               Micro-Doppler Classification on Noisy Intermediate-Scale
+               Quantum ({NISQ}) Hardware},
+  journal   = {arXiv preprint},
+  volume    = {arXiv:2601.22194},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2601.22194}
+}
+```
+
+**Radar-Based ATR Framework** (foundational signal model and SVM classifier):
+```bibtex
+@article{agnihotri2020radar,
+  author    = {Agnihotri, Vikas and Sabharwal, Munish},
+  title     = {An Automatic Radar Based Aerial Target Recognition Framework},
+  journal   = {Journal of Interdisciplinary Mathematics},
+  volume    = {23},
+  number    = {2},
+  pages     = {321--333},
+  year      = {2020},
+  doi       = {10.1080/09720502.2020.1737377},
+  url       = {https://doi.org/10.1080/09720502.2020.1737377}
+}
+```
+
+**Frequency Effects on Micro-Doppler** (radar frequency analysis underpinning the extended dataset):
+```bibtex
+@inproceedings{agnihotri2019frequency,
+  author    = {Agnihotri, Vikas and Sabharwal, Munish and Goyal, Vinay},
+  title     = {Effect of Frequency on Micro-Doppler Signatures of a Helicopter},
+  booktitle = {2019 International Conference on Advances in Big Data,
+               Computing and Data Communication Systems (icABCD)},
+  year      = {2019},
+  doi       = {10.1109/ICABCD.2019.8851024},
+  url       = {https://doi.org/10.1109/ICABCD.2019.8851024}
+}
+```
+
+---
+
+## 📚 References
+
+### Author's Publications
+
+1. Agnihotri, V., Kaur, J., & Kaushik, S. (2026). *Practical Evaluation of Quantum Kernel Methods for Radar Micro-Doppler Classification on NISQ Hardware*. [arXiv:2601.22194](https://arxiv.org/abs/2601.22194)
+2. Agnihotri, V., & Sabharwal, M. (2020). *An automatic radar based aerial target recognition framework*. Journal of Interdisciplinary Mathematics, 23(2), 321–333. [DOI: 10.1080/09720502.2020.1737377](https://doi.org/10.1080/09720502.2020.1737377)
+3. Agnihotri, V., Sabharwal, M., & Goyal, V. (2019). *Effect of frequency on micro-Doppler signatures of a helicopter*. 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). [DOI: 10.1109/ICABCD.2019.8851024](https://doi.org/10.1109/ICABCD.2019.8851024)
+
+### Background Literature
+
+4. Chen, V.C., Li, F., Ho, S.S., & Wechsler, H. (2006). *Micro-Doppler effect in radar: phenomenon, model, and simulation study*. IEEE TAS.
+5. Molchanov, P., et al. (2015). *Classification of small UAVs and birds by micro-Doppler signatures*. IET Radar.
+6. Bendale, A., & Boult, T.E. (2016). *Towards open set deep networks (OpenMax)*. CVPR.
+7. Liu, W., et al. (2020). *Energy-based out-of-distribution detection*. NeurIPS.
+8. Schuld, M., & Petruccione, F. (2021). *Machine Learning with Quantum Computers*. Springer.
+
+---
+
+<div align="center">
+<sub>Made with ❤️ for the radar signal processing and AI community</sub>
+</div>