README.md

metadata

license: apache-2.0
task_categories:
  - time-series-forecasting
  - robotics
tags:
  - uav
  - drone
  - navigation
  - gps-denied
  - electronic-warfare
  - sensor-fusion
  - kalman-filter
  - inertial-navigation
  - physics-simulation
  - ukraine
  - military
  - defense
language:
  - en
pretty_name: UAV Navigation Resilience Benchmark
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl

UAV Navigation Resilience Benchmark

Dataset Description

High-fidelity UAV navigation simulation data with realistic Electronic Warfare (EW) jamming scenarios based on OSINT profiles of Russian military systems deployed in Ukraine.

Why This Dataset?

Most UAV navigation datasets are "fair weather" - they don't include adversarial conditions. This dataset fills that gap:

• GPS Jamming: Realistic signal denial based on Zhitel, Pole-21, and Krasukha-4 specifications
• INS Drift: Allan variance-based MEMS IMU modeling shows real navigation degradation
• Ground Truth Labels: Every sample has true position for supervised learning
• Scenario Diversity: From nominal flight to complete GPS denial

Key Statistics

Metric	Value
Total Samples	1,820,400
Sample Rate	10 Hz
Scenarios	11
Features	20 columns
Physics	Full 6-DOF dynamics
IMU Model	Allan variance (tactical grade)

Schema (Nested JSON)

Each sample is a nested JSON object designed for both Supervised Learning and Reinforcement Learning:

{
  "meta": {"run_id": "...", "scenario": "...", "instance": 0, "batch": 1},
  "timestamp_s": 45.3,
  "ground_truth": {"pos_ned_m": [x,y,z], "vel_ned_mps": [vx,vy,vz]},
  "sensors": {
    "imu": {"accel_body_mps2": [...], "gyro_body_rads": [...]},
    "gps": {"valid": true, "cn0_dbhz": 45, "hdop": 1.2, "num_sats": 10}
  },
  "environment": {"jammer_active": false, "jammer_distance_m": 5000, "jammer_power_dbm": 30},
  "baseline": {"ukf_pos_est": [...], "ukf_pos_error_m": 2.5}
}

Key Fields

Path	Type	Description
ground_truth.pos_ned_m	[3] float	True position (labels)
sensors.imu.accel_body_mps2	[3] float	Raw accelerometer (with bias/noise)
sensors.imu.gyro_body_rads	[3] float	Raw gyroscope (with bias/noise)
sensors.gps.cn0_dbhz	float	Signal strength (key for jamming detection)
sensors.gps.hdop	float	Dilution of precision
environment.jammer_active	bool	EW label for classification
baseline.ukf_pos_error_m	float	UKF error to beat

Scenarios

Scenario	Samples	Avg Error	Description
baseline_nominal	126,000	94.8m
baseline_urban	222,000	94.8m
stress_complete_gps_denial	222,000	6480.6m
stress_rapid_maneuvers	50,400	977.2m
stress_sensor_degradation	126,000	3496.5m
ukraine_assault_heavy_ew	133,200	6430.2m
ukraine_coastal_crimea	252,000	3005.8m
ukraine_recon_light_ew	252,000	4105.5m
ukraine_spoofing_attack	126,000	1388.9m
ukraine_trench_warfare	88,800	6491.8m
ukraine_urban_moderate	222,000	2966.1m

Usage

from datasets import load_dataset

# Load dataset
ds = load_dataset("chkmie/uav-resilience-bench")

# Filter Ukraine scenarios
ukraine = ds['train'].filter(lambda x: 'ukraine' in x['meta']['scenario'])

# Get jamming periods
jammed = ds['train'].filter(lambda x: x['environment']['jammer_active'])

# Extract features for ML
sample = ds['train'][0]
imu_accel = sample['sensors']['imu']['accel_body_mps2']
gps_valid = sample['sensors']['gps']['valid']
true_pos = sample['ground_truth']['pos_ned_m']

Use Cases

1. GPS Denial Detection: Binary classification of jamming_active
2. State Estimation: Regress true_pos from est_pos + context
3. Filter Training: Learn to reduce position_error_m
4. Anomaly Detection: Detect navigation degradation onset
5. RL Filter Selection: Train policy to switch navigation modes

Technical Details

Simulation Framework

• Dynamics: 6-DOF quadcopter model
• IMU: MEMS tactical grade with Allan variance noise
• GPS: Constellation-based with HDOP effects
• Filter: 15-state Unscented Kalman Filter
• EW: RF propagation-based jamming effectiveness

EW Systems Modeled

System	Type	Range	Power
Zhitel (R-330Zh)	Jamming	30 km	20 dBm
Pole-21	Jamming	25 km	30 dBm
Krasukha-4	Jamming	150 km	50 dBm
Trench Jammer	Portable	5 km	15 dBm

Citation

@dataset{foss2026uavresilience,
  author = {Foss, David},
  title = {UAV Navigation Resilience Benchmark},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/chkmie/uav-resilience-bench}},
  note = {ORCID: 0009-0004-0289-7154}
}

License

Apache 2.0

Author

David Foss - ORCID: 0009-0004-0289-7154

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Physics-based simulation dataset - Commercial licensing available