Abstract
This paper presents a system-level analysis of the navigation errors in an autonomous UAV carrier-landing scenario with a focus on fixed-wing aircraft. The research identifies relationships between relative state estimation errors and navigation system parameters. The method for achieving this objective is to construct a Monte Carlo simulation of the UAV landing on the aircraft carrier. An indirect Extended Kalman Filter is used in the simulation to estimate the position, velocity, and attitude of both the UAV and aircraft carrier. Results of the simulation focus on the relative position, velocity and attitude covariances at three seconds prior to touch down. The sensitivity of these covariances to measurement availability and frequency is illustrated as a function of IMU grade and camera errors. The results identified in this research enables designers to select system components which minimize cost while maintaining mission-specific navigation error requirements.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call