electricsheepafrica/radar-multimodal-edge

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Multi-Modal Edge Sensor Signatures (Radar + IMU + Acoustic)

Dataset Description

Synthetic multi-modal sensor fusion dataset combining radar, inertial measurement unit (IMU), and acoustic signatures for edge-deployed threat classification nodes. Contains 2,100 labeled samples across 7 target classes, with features extracted from all three modalities plus cross-modal coherence measures. Designed for scenarios where any single sensor modality may be degraded (RF jamming, acoustic noise, poor ground coupling).

Dataset Summary

Property	Value
Total samples	2,100 (300 per class, perfectly balanced)
Classes	7
Features	44 columns (40 numeric, 4 categorical/ID)
Sensors	X-band radar + MEMS IMU (MPU6050) + acoustic microphone
Range	100 m to 3 km
SNR range	0 to 47 dB
Format	Apache Parquet

Sensor Specifications

Sensor	Parameter	Value
Radar	Type	X-band phased array
	Frequency	9.5 GHz
	PRF	20 kHz
	CPI	256 pulses
IMU	Model	MPU6050 (MEMS accelerometer)
	Sample rate	1,000 Hz
	Range	+/- 16 g
	Noise density	400 ug/sqrt(Hz)
	Axis	Z (vertical, ground vibration)
Acoustic	Type	Microphone
	Sample rate	8,000 Hz
	Sensitivity	-38 dB
	Noise floor	30 dB SPL

Target Classes

Class	Group	Category	Radar RCS (m^2)	Key IMU Signature	Key Acoustic Signature
drone_air	1	Threat	0.01	267/534 Hz rotor harmonics (0.002 g)	267/534/801 Hz harmonics
drone_ground	1	Threat	0.01	267/534 Hz coupled (0.05 g, near-field)	267/534 Hz (louder, close)
vehicle_ground	2	Threat	10.0	30/60/90/120 Hz engine (0.5 g, strong)	80/160/240 Hz engine
personnel	2	Threat	1.0	1.5/3.0 Hz gait (0.01 g, weak)	200/400/800 Hz footsteps
aircraft_distant	0	Clutter	50.0	0.5 Hz (barely detectable)	100/200 Hz (faint)
wind_clutter	0	Clutter	distributed	5/15/30 Hz broadband	100/200/400 Hz broadband
rain_clutter	0	Clutter	distributed	50/100/200 Hz impact	1000/2000/4000 Hz

Multi-Modal Signal Generation

Radar

Range-Doppler maps are generated with the same coherent pulse-Doppler pipeline as other datasets. Class-specific micro-Doppler (rotor, vibration, distributed) is injected as amplitude-modulated sidebands. Environmental effects (attenuation, multipath scintillation) are applied to the RD power map.

IMU (Vibration)

Synthetic accelerometer signals are generated by summing sinusoidal components at class-specific vibration frequencies with:

• Harmonic content (2nd harmonics at 30% amplitude)
• Frequency jitter (+/- 5% variation per component)
• Amplitude variation (+/- 50% per component)
• MEMS noise floor (400 ug/sqrt(Hz))
• Range-dependent ground coupling attenuation: 1 / (1 + range/50m)

Acoustic

Acoustic signals are synthesized from class-specific frequency components with:

• SPL-calibrated amplitudes: A = 10^(SPL/20) * 20 uPa
• Frequency jitter (+/- 2%)
• Ambient noise floor (30 dB SPL)
• Inverse-distance attenuation

Environmental Effects on Modalities

• Wind/rain on acoustic: Environments with high clutter spectral width add broadband noise
• Urban on IMU: High-multipath environments add ambient ground vibration
• Rain on radar: Rain reflectivity raises the effective noise floor

Features

Radar Features (15)

• rd_peak_power_db: Peak power in RD map (dB)
• rd_peak_range_bin: Range bin of peak
• md_mean_doppler_hz, md_std_doppler_hz: Doppler spectral center and spread
• md_energy_spread: Energy concentration in top signal bins
• md_dominant_freq_hz: Peak Doppler frequency
• bf_flash_rate_hz: Blade flash periodicity
• bf_max_doppler_spread_hz: Maximum Doppler spread from config
• bf_modulation_index: Sideband-to-main-lobe energy ratio
• st_md_bandwidth_hz: 90% energy bandwidth (peak-centered)
• st_md_periodicity_hz: Envelope FFT periodicity
• st_md_contrast: Peak-to-median power contrast
• snr_db: Signal-to-noise ratio (0-55 dB)
• rcs_estimated_m2, rcs_true_m2: Estimated and ground-truth RCS

IMU Features (10)

• imu_peak_freq_hz: Dominant vibration frequency
• imu_dominant_amplitude_g: Peak spectral amplitude (g)
• imu_spectral_centroid_hz: Energy-weighted mean frequency
• imu_bandwidth_hz: 3-dB bandwidth
• imu_rms_g: RMS acceleration
• imu_crest_factor: Peak-to-RMS ratio
• imu_num_peaks: Number of spectral peaks above 10% of max
• imu_low_freq_energy: Fractional energy 0-50 Hz
• imu_mid_freq_energy: Fractional energy 50-200 Hz
• imu_high_freq_energy: Fractional energy 200-500 Hz

Acoustic Features (9)

• ac_peak_freq_hz: Dominant acoustic frequency
• ac_spl_db: Sound pressure level (relative dB)
• ac_spectral_centroid_hz: Energy-weighted mean frequency
• ac_bandwidth_hz: 3-dB bandwidth
• ac_harmonic_ratio: Fraction of peaks at harmonic multiples
• ac_num_harmonics: Detected harmonic peaks
• ac_low_freq_energy: Fractional energy 0-500 Hz
• ac_mid_freq_energy: Fractional energy 500-2000 Hz
• ac_high_freq_energy: Fractional energy 2000-4000 Hz

Cross-Modal Features (3)

• xmodal_radar_imu_coherence: Radar-IMU spectral correlation [-1, 1]
• xmodal_radar_ac_coherence: Radar-acoustic spectral correlation [-1, 1]
• xmodal_imu_ac_coherence: IMU-acoustic spectral correlation [-1, 1]

Scenario Metadata (4)

• range_m: Target range (m)
• velocity_ms: Radial velocity (m/s)
• environment: Environment condition
• jamming_type: EW type (always "none" for edge nodes)

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/radar-multimodal-edge")
df = ds['train'].to_pandas()

# Modality-specific feature groups
radar_features = [c for c in df.columns if c.startswith(('rd_', 'md_', 'bf_', 'st_', 'snr'))]
imu_features = [c for c in df.columns if c.startswith('imu_')]
acoustic_features = [c for c in df.columns if c.startswith('ac_')]
cross_modal = [c for c in df.columns if c.startswith('xmodal_')]

# Sensor fusion: use all modalities
all_features = radar_features + imu_features + acoustic_features + cross_modal + ['rcs_estimated_m2']
X = df[all_features].values

Data Quality

• Perfectly balanced: 300 samples per class
• No NaN or Inf values
• RCS estimation correlates with ground truth (r ~ 0.94)
• Micro-Doppler features decoupled from peak power via peak-centered windowing
• Cross-modal coherence reflects genuine signal structure correlation, not artifacts
• Environment effects propagate realistically across all three modalities

Citation

@misc{electricsheep2025multimodal,
  title={Multi-Modal Edge Sensor Signatures: Radar, IMU, and Acoustic Fusion for Threat Classification},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/radar-multimodal-edge}
}