On the design of an optimal GNC sensor architecture for autonomous planetary landers

The definition of guidelines for the design of an optimal GNC sensor architecture for spacecraft is a recurrent problem. Since the beginning of Space Age several generations of GNC sensors have been developed, flight tested and successfully used in space missions. With the advent of new technologies and materials, GNC sensors performances have significantly improved on previous decades and new sensors have emerged. Concepts are evolving from ground-based to autonomous GNC systems. Hazard avoidance systems for planetary landers are replacing hazard tolerance systems. Both Lidar-based, and Vision-based GNC systems are currently being developed to support autonomous landings. The optimum mix of sensors and the best way to fuse sensor data is an important area of research for future planetary lander space missions. Kalman filters play an important role in fusing data from several sensors to give an optimal estimate of the dynamic state of the spacecraft system. This paper briefly introduces Kalman filters and then gives a series of examples to demonstrate their use. It then goes on to describe current research at the University of Dundee on GNC sensor architectures for autonomous planetary landers.