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

Abstract

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

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

“…In sensor networks, the problem of estimating the state observed by multiple sensors has been analyzed extensively in recent decades due to the variety of applications they have in signal processing (see, e.g., [1][2][3][4][5][6][7][8][9]).…”