Navigation Uncertainty & Covariance Analysis

Analyze position uncertainty growth under different sensor configurations and failure scenarios

About This Demo

The covariance matrix tracks navigation uncertainty in the Kalman filter. This tool analyzes how uncertainty grows without aiding and how different sensors reduce uncertainty. Compare scenarios to understand sensor redundancy, failure modes, and observability. The covariance trace shows total position uncertainty - lower is better.

Sensor Configuration Scenarios


All Sensors Active

DVL Failure

LBL Failure

INS Only

INS + DVL Only

INS + LBL Only

Uncertainty Evolution Comparison

Multi-Scenario Comparison

Error Ellipse Evolution

Scenario Performance Summary

Scenario Position Error @ 1hr Velocity Error @ 1hr Operational Status
All Sensors 2.3 m 0.02 m/s Excellent
DVL Failure 45 m 0.15 m/s Degraded - LBL required
LBL Failure 8 m 0.03 m/s Good with DVL
INS Only 800 m 1.2 m/s Dead reckoning only
INS + DVL 12 m 0.02 m/s Good for short missions
INS + LBL 3.5 m 0.08 m/s Acceptable