Fusion of Multiple Terrain-Based Sensors for Descent-To-Landing Navigation


This paper investigates the ability of a navigation filter to process multiple terrain-based sensors, such as slant-range, slant-speed, and terrain relative navigation sensors, during a descent-to-landing scenario to estimate the state of the landing vehicle. The filtering technique leveraged is based upon a factorized form of the multiplicative extended Kalman filter, and terrain-based measurements are fused with star camera and inertial measurement unit measurements to estimate position, velocity, and attitude of the landing vehicle. Monte Carlo simulations of the filter are carried out to assess the performance of the navigation filter along a lunar descent trajectory. It is found that the presented navigation filter can successfully process multiple terrain-based sensor outputs and that including terrain information can improve navigation precision.

Meeting Name

AIAA Scitech Forum, 2019 (2019: Jan. 7-11, San Diego, CA)


Mechanical and Aerospace Engineering

Keywords and Phrases

Aviation; Intelligent systems; Landforms; Landing; Monte Carlo methods; Navigation, Descent trajectories; Filtering technique; Inertial measurement unit; Multiplicative extended kalman filters; Navigation filters; Navigation precision; Relative navigation; Sensor output, Kalman filters

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Jan 2019