Ground Alignment Error Bounds for Inertial Navigation Initialization
Ground alignment error bounds depend on accelerometer turn-on bias for leveling and East gyro bias stability divided by cosine latitude for heading accuracy.
Kalman Filter Convergence
Iterative Process
Process by which an iterative estimation algorithm reaches a stable state where the calculated covariance matches the actual error in the system state. This kalman filter convergence ensures that the navigation solution becomes increasingly accurate as more measurements are processed over time. The transition represents the movement from an initial state of high uncertainty to a reliable estimate of position and velocity.
State Estimation
Mathematical models use weighted averages of predicted and observed data to update the current status of the vehicle. During kalman filter convergence, the gain of the filter adjusts to prioritize different data sources based on their known noise characteristics. This dynamic tuning allows the system to recover from initial errors or sudden changes in the movement of the platform.
The algorithm effectively learns the characteristics of the sensor noise as it processes more information.
Time Requirement
Speed of the process is governed by the sample rate of the sensors and the magnitude of the initial uncertainty. Achieving kalman filter convergence might take several seconds or minutes depending on the complexity of the system and the quality of the incoming data. In high-dynamic environments, the filter must converge rapidly to prevent the navigation solution from diverging from the true path.
Stability Metric
Monitoring the residual values provides a way to verify that the filter is operating correctly. If kalman filter convergence is not achieved, the resulting data may be erratic or completely incorrect.