Data Analysis Techniques for a Lunar Surface Navigation System Testbed

Abstract NASA is interested in finding new methods of surface navigation to allow astronauts to navigate on the lunar surface. In support of the Vision for Space Exploration, the NASA Glenn Research Center developed the Lunar Extra-Vehicular Activity Crewmember Location Determination System and performed testing at the Desert Research and Technology Studies event in 2009. A significant amount of sensor data was recorded during nine tests performed with six test subjects. This paper provides the procedure, formulas, and techniques for data analysis, as well as commentary on applications. 1.0 Introduction The National Aeronautics and Space Administration (NASA) Vision for Space Exploration, announced by President George W. Bush in 2004, calls for the safe return of astronauts to the Moon. In support of the Vision, NASA is considering methods of surface navigation for crewmembers performing extra-vehicular activity (EVA). The Digital Communications and Navigation Branch at the NASA Glenn Research Center (GRC) has developed a mobile navigation testbed, the Lunar Extra-Vehicular Activity Crewmember Location Determination System (LECLDS), to evaluate techniques for human exploration. LECLDS consists of a Global Positioning System (GPS) receiver, an inertial measurement unit (IMU), a digital compass, a navigation processor, and a handheld display unit. LECLDS was demonstrated at the 2009 NASA Desert Research and Technology Studies (D-RATS) event held at the Black Point Lava Flow near Flagstaff, Arizona. During the D-RATS demonstration, test subjects used the navigation testbed to walk between pre-determined waypoints. Data was collected to gauge the test subject’s ability to navigate (Measures of Effectiveness, MoEs) as a function of a level of performance (Measures of Performance, MoPs) of the LECLDS testbed. Navigation data was displayed in real-time to the test subject, and raw measurements were recorded simultaneously for post-processing. This report covers the steps taken to analyze data from D-RATS 2009. This includes a brief overview of the log formats and time alignment strategy used for LECLDS, as well as the manual techniques needed to reduce the data set to useful segments. A number of data analysis metrics are presented, with commentary on applications. Finally, recommendations are made for ways to improve the collected data to make the analysis more reliable.