A Distributionally Robust Fusion Framework for Autonomous Multisensor Spacecraft Navigation during Entry Phase of Mars Entry, Descent, and Landing

A robust multisensor navigation filter design for the entry phase of next-generation Mars entry, descent, and landing (EDL) is presented. The longest most uncertain portion a sequence. Navigation performance at this stage determines precision end powered descent EDL. In present work, measurements from ground-based radio beacon array, an inertial measurement unit (IMU), as well array atmospheric aerothermal sensors on body vehicle are fused using M-estimation-based iterated extended Kalman filtering (MIEKF) framework. approach enables increased positioning accuracy estimation parameters that otherwise unobservable. Furthermore, owing to proposed statistically formulation, states can be accurately estimated in presence non-Gaussian noise. Deviations normally distributed observation noise correspond outlier events such sensor faults or other sources spurious data interference. framework provides significant reduction error parachute EDL, thereby increasing likelihood pinpoint chosen site. Six three estimated. suggested method compared (EKF) unscented (UKF). Detailed simulation results show presented fusion architecture able meet future planetary requirements realistic scenarios.