Moon Landing based on Multi-Sensor Fusion of Lunar Navigation Satellites and Onboard Sensor observables
- Paper number
IAC-24,B2,IPB,5,x84896
- Author
Dr. Luca Andolfi, Telespazio S.p.A., Italy
- Coauthor
Mr. Luca Ostrogovich, University of Naples "Federico II", Italy
- Coauthor
Mr. Michele Ceresoli, Politecnico di Milano, Italy
- Coauthor
Mr. Simone Giannattasio, Telespazio S.p.A., Italy
- Coauthor
Mr. Marco Brancati, Telespazio, Italy
- Coauthor
Mr. Roberto Del Prete, University of Naples "Federico II", Italy
- Coauthor
Mr. Arsenio Maria Di Donna, Telespazio S.p.A., Italy
- Coauthor
Prof. Michele Grassi, University of Naples "Federico II", Italy
- Coauthor
Prof. Michèle Lavagna, Politecnico di Milano, Italy
- Coauthor
Prof. Alfredo Renga, University of Naples "Federico II", Italy
- Coauthor
Dr. Giuseppe Tomasicchio, Telespazio, Italy
- Coauthor
Dr. Giovanni Zanotti, Politecnico di Milano, Italy
- Year
2024
- Abstract
Moon exploration has become a relevant new domain for a new age of Space commercialization, where a wide range of public and private enterprises, are increasing in the last years their interest and investments in lunar missions, leading NASA, ESA and JAXA to start their exploration initiatives. Recently many studies have been conducted to demonstrate the improvements in terms of Position, Velocity and Timing (PVT) estimation for the users enabled by a Navigation Constellation around the Moon. The objective of this study is to describe an approach that utilizes sensor-fusion techniques based on an tightly-coupled Extended Kalman Filter (EKF) to integrate lunar GNSS-like one-way ranging signals with a wide range of on-board observables, such as Inertial Measurement Units (IMU), altimeters, Two-Way Ranging (TWR) with the Lunar Orbit Platform - Gateway (LOP-G), and Vision-Based Navigation (VBN) techniques for the estimation of both the spacecraft state and the receiver clock bias and drift. This work integrates the complete VBN pipeline within the EKF to improve the PVT estimation of the user. The selected lunar landing mission use case requires demanding navigation accuracy performance to reach the target landing area, in this case the Shackleton Rim. The VBN algorithms applied for this study, implement Deep Learning techniques and are based on seleno-referenced, high-fidelity lunar images generated through an innovative tool developed in the Telespazio Concurrent and Collaborative Design Facility (C2DF), for supporting the {\it Interactive Mission Modeling, Visualization/Validation} (IMMV$^2$) functionality. It combines the capabilities of various 3D modelling, simulation, and visualization/gaming software, and includes recent developments on a Visual Scenario Generator (VSG) module which allows to enable further capabilities, such as: i) image dataset pre-processing, ii) evaluation of the crater detector performance with varying lighting conditions and noise levels, iii) assessment of the extension of the lunar crater image catalogue, exploiting the crater detection capabilities with Artificial Intelligence (AI) algorithms, iv) extension and augmentation of the image dataset, and v) fine-tuning of the crater matching parameters to improve the VBN accuracy figures. In conclusion, this paper will present the results in terms of the multi-sensor fusion PVT performance considering different combinations of observables, including VBN measurements and Lunar satellite constellation pseudoranges. Finally, an assessment of the robustness of the computation of a very accurate estimation of the lander position and velocity, along all the different phases of its trajectory, in several configurations and availability conditions, will be performed.- Abstract document
- Manuscript document
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