oMMP 4D Flight Path Visualization

oMMP 4D Timeline: Radar Tracking Visualization

Converting 20 Hours of GPS/Radar Data into Scientific Precision Flight Paths

Flight Data Controls

100x
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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