oMMP 4D Timeline: Radar Tracking Visualization
Converting 20 Hours of GPS/Radar Data into Scientific Precision Flight Paths
Flight Data Controls
100x
9
4D Flight Path Visualization
Current Position Data:
Time: --:--:--
Latitude: --.------°
Longitude: ---.------°
Altitude: -----m
Velocity: ---m/s
Acceleration: --m/s²
oMMP Data Transformation
Raw GPS/Radar Data → oMMP Record
Input: Raw Radar Data Stream
// Example: 20 hours of radar tracking data { "track_id": "TRK-2024-0847", "duration_hours": 20, "sample_rate_hz": 1, "data_points": [ {"t": 0, "lat": 40.7128, "lon": -74.0060, "alt": 10000, "v": 180}, {"t": 1, "lat": 40.7130, "lon": -74.0058, "alt": 10002, "v": 182}, // ... 72,000 data points (20 hours × 3600 seconds) ] }
Output: oMMP Scientific Precision Record
{ "oMMP_record": { "o": { "id": "radar_station_JFK", "substrate": "electromagnetic_sensor", "spacetime_bounds": { "start": [40.712800000, -74.006000000, 10000.000, 1234567890.000], "end": [41.247839562, -73.498726341, 15234.567, 1234639890.000] } }, "MMP": { "trajectory_4d": { "path_string": "40.712800000,-74.006000000,10000.000,0;40.713000000,-74.005800000,10002.000,1;...", "scientific_notation": { "positions": "4.07128e1,-7.40060e1,1.0000e4", "velocities": "1.80000e2,2.50000e0,2.00000e0", "accelerations": "2.00000e0,1.50000e-1,0.00000e0" }, "path_signature": { "total_distance_m": 2847562.34567890, "avg_velocity_ms": 198.23456789, "max_acceleration_ms2": 45.67890123, "anomaly_score": 0.847362951 } } } } }
Flight Path Pattern Analysis
Kinematic Signature
Velocity profile reveals propulsion characteristics
Altitude Profile
Vertical movement patterns indicate intent
Turn Analysis
G-force calculations reveal craft capabilities
Scientific Precision & Compression
How oMMP Achieves Maximum Precision with Minimum Storage
The framework uses several techniques to maintain scientific precision while compressing 20 hours of tracking data:
1. Differential Encoding
Store deltas instead of absolute positions:
Δlat = lat[i] - lat[i-1]
Reduces storage by 60-80%
2. Adaptive Precision
Use variable decimal places based on velocity:
precision = min(15, 9 + log10(1/velocity))
Fast movement = less precision needed
3. Polynomial Fitting
Smooth trajectories as polynomial curves:
path(t) = a₀ + a₁t + a₂t² + a₃t³
20 hours → 100 polynomial segments