Analyze the Impact of Sensor Error Modelling on Navigation Performance
Compute Coverage
Compute mean orientation error
Compute mean position error
Compute Normalized Estimation Errror Squared (NEES)
Construct a sensor object
Construct a timing object
Construct a trajectory object
Runs "IMU model evaluation" or "INS-GPS-Baro integrated navigation (se...
Plot multiple coverage.stat objects
Plot a navigation object
Plot multiple navigation.stat objects
Plot multiple nees.stat objects
Plot a trajectory object
Plot IMU error with covariances
Plot navigation states with covariance
Print a sensor object parameters (name, frequency and error model)
Print trajectory Objects
Transform position from ellipsoidal to NED coordinates
Transform position from NED to ellipsoidal coordinates
Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.
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