--- license: apache-2.0 task_categories: - tabular-classification language: - en tags: - radar - swarm-detection - intent-recognition - drone-swarm - formation-analysis - behavioral-classification - synthetic-data - counter-uas - synthetic pretty_name: Synthetic Swarm Intent Recognition size_categories: - 1K ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. # Synthetic Swarm Intent Recognition ## Dataset Description Synthetic radar dataset for classifying coordinated drone swarm behavior and inferring tactical intent from aggregate radar observables. Contains 3,000 labeled samples across 6 swarm behavior classes, each with both a class label and an intent label. Features capture formation geometry, velocity distributions, behavioral dynamics, and aggregate Doppler statistics from multi-target range-Doppler maps. ### Dataset Summary | Property | Value | |----------|-------| | **Total samples** | 3,000 (500 per class, perfectly balanced) | | **Classes** | 6 | | **Intent labels** | 6 (one-to-one with classes) | | **Features** | 29 columns (24 numeric, 5 categorical/ID) | | **Radar system** | X-band, 9.5 GHz | | **Range** | 500 m to 8 km | | **SNR range** | 10 to 29 dB | | **Swarm sizes** | 1 to 60 members | | **Format** | Apache Parquet | ## Swarm Behavior Classes | Class | Intent Label | Formation | Targets | Speed (m/s) | Behavior | Maneuver Rate | |-------|-------------|-----------|---------|-------------|----------|---------------| | `recon` | reconnaissance | spread | 3-12 | 20 +/- 5 | grid_search | 0.1 Hz | | `attack` | attack | wedge | 8-30 | 40 +/- 10 | converge | 0.5 Hz | | `jamming` | electronic_warfare | ring | 2-8 | 15 +/- 5 | orbit | 0.3 Hz | | `decoy` | deception | dispersed | 10-40 | 25 +/- 8 | erratic | 1.0 Hz | | `evasion` | evasion | scatter | 4-15 | 35 +/- 10 | evade | 2.0 Hz | | `individual` | individual | none | 1 | 25 +/- 10 | straight | 0.1 Hz | ## Signal Generation Each sample generates a multi-target range-Doppler map: 1. **Formation geometry**: Targets are placed according to the formation type: - **Spread/dispersed**: Random positions within formation radius - **Wedge**: Angular spread with range tapering (attack formation) - **Ring**: Targets equally spaced on a circle (jamming standoff) - **Scatter**: Random positions at 0.5-1.5x formation radius (evasion) 2. **Per-target Doppler**: Velocity varies by behavior type: - **Converge**: Members accelerate toward a common point - **Scatter**: Members diverge with high velocity spread - **Orbit**: Sinusoidal velocity variation (circular path) - **Evade**: Large random velocity offsets - **Straight**: Tight velocity clustering 3. **Multi-target pulse matrix**: Each swarm member contributes `amp * exp(j * phase)` at its range bin and Doppler frequency. The per-target RCS follows log-normal statistics. 4. **Environment**: Ground clutter with environment-dependent amplitude and Doppler spread. Two-way atmospheric and rain attenuation applied to signal bins. Multipath scintillation on detected targets. 5. **Feature extraction**: CFAR-like detection (10 dB above median noise), noise-thresholded Doppler profiles (median + 3*MAD), and behavioral feature derivation from RD map dynamics. ## Features ### Aggregate Radar (5) - `rd_total_power_db`: Total integrated power in RD map (dB) - `rd_num_detected_targets`: CFAR-detected target count - `rd_range_extent_m`: Range extent from profile standard deviation - `rd_doppler_extent_hz`: Doppler extent from CFAR-detected bins (Hz) - `rd_mean_rcs_m2`: Mean per-target RCS from config (m^2) ### Swarm Formation (7) - `sw_num_targets`: Number of swarm members - `sw_formation_radius_m`: Formation radius (m) - `sw_formation_type`: Formation geometry (spread/wedge/ring/scatter/dispersed/none) - `sw_centroid_range_m`: Swarm centroid range (m) - `sw_centroid_velocity_ms`: Swarm centroid velocity (m/s) - `sw_velocity_spread_ms`: Velocity dispersion across members (m/s) - `sw_range_spread_m`: Range spread of detected targets (m) ### Aggregate Micro-Doppler (6) - `md_mean_doppler_hz`: Mean Doppler from thresholded profile (Hz) - `md_std_doppler_hz`: Doppler spread from thresholded profile (Hz) - `md_bandwidth_hz`: 90th-10th percentile Doppler bandwidth (Hz) - `md_energy_spread`: Peak energy concentration - `st_md_periodicity_hz`: Dominant periodicity from envelope FFT (Hz) - `st_md_contrast`: Peak-to-median power contrast ### Behavioral (4) - `bh_maneuver_rate_hz`: Rate of formation changes (Hz) — derived from behavior type with noise - `bh_altitude_var_m`: Altitude variance across swarm (m) — derived from range extent - `bh_speed_var_ms`: Speed variance across swarm (m/s) — derived from Doppler std - `bh_heading_var_deg`: Heading variance across swarm (deg) — behavior-dependent ### Scenario (3) - `snr_db`: Signal-to-noise ratio from actual RD map (dB) - `range_m`: Scenario range (m) - `environment`: Environment condition ## Key Discriminating Features | Feature | Recon | Attack | Jamming | Decoy | Evasion | Individual | |---------|-------|--------|---------|-------|---------|------------| | Targets | 3-12 | 8-30 | 2-8 | 10-40 | 4-15 | 1 | | Speed | Low | High | Low | Medium | High | Medium | | Heading var | Low | Medium | Low | High | Very high | Very low | | Maneuver rate | Very low | Medium | Low | High | Very high | Very low | | Formation | Spread | Wedge | Ring | Dispersed | Scatter | None | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/radar-swarm-intent") df = ds['train'].to_pandas() # Dual-label classification print(df[['class_label', 'intent_label']].drop_duplicates()) # Formation type distribution print(df['sw_formation_type'].value_counts()) # Behavioral analysis behavioral = ['bh_maneuver_rate_hz', 'bh_altitude_var_m', 'bh_speed_var_ms', 'bh_heading_var_deg'] print(df.groupby('class_label')[behavioral].mean()) ``` ## Data Quality - Perfectly balanced: 500 samples per class - No NaN or Inf values - SNR derived from actual RD map (not random) - Swarm target counts consistent with config ranges - Doppler features use noise-thresholded profiles (median + 3*MAD) - Behavioral features show expected class separation (evasion has highest heading variance, individual has lowest maneuver rate) ## Citation ```bibtex @misc{electricsheep2025swarm, title={Synthetic Swarm Intent Recognition: Radar-Based Behavioral Classification of Coordinated Drone Formations}, author={Electric Sheep Africa}, year={2025}, publisher={HuggingFace}, url={https://huggingface.co/datasets/electricsheepafrica/radar-swarm-intent} } ```