Update README.md

README.md

@@ -1,3 +1,105 @@
 ---
 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
 ---
+
+
+# Micro-Doppler Signatures 
+
+# Micro-Doppler Signatures - Helicopter Classification Dataset
+
+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.
+
+## Dataset Description
+
+### What's in it
+
+Two CSV files, each with 100,000 samples:
+
+| 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) |
+
+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.
+
+
+### Target classes
+
+| 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 |
+
+### Signal model
+
+The micro-Doppler return from the k-th blade is modelled as a sinc function with phase modulation from the rotating blade geometry. 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
+    - ```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}
+      }
+      ```
+
+      ## License
+
+      MIT - free to use for research and commercial purposes with attribution.
+