Datasets:

bukac82
/

MicroDopplerSignatures

---
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 - 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:

$$s_k(t) = L \cdot \text{sinc}\!\left(\frac{2L}{\lambda}\cos\phi_k\cos\beta\right) \exp\!\left(j\frac{4\pi L}{\lambda}\cos\phi_k\cos\beta\right)$$

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.