In support of NASAâs Autonomous Landing and Hazard Avoidance Technology project, an extended Kalman filter (EKF) routine has been developed for estimating the position, velocity, and attitude of a spacecraft during the landing phase of a planetary mission. The EKF is a recursive algorithm for obtaining the minimum variance estimate of a nonlinear dynamic process from a sequence of noisy observations. The proposed filter combines measurements of acceleration and angular velocity from an inertial measurement unit with range and range-rate observations from an onboard light detection and ranging (LIDAR) system. These high-precision LIDAR measurements of distance to the ground and approach velocity will enable both robotic and manned vehicles to land safely and precisely at scientifically interesting sites. The robustness and accuracy of the Kalman filter were first established using a simplified simulation of the final translation and touchdown phase of the Apollo lunar landings. In addition, experimental results from a helicopter flight test performed at NASA Dryden in August 2008 demonstrate the merit in employing LIDAR for pinpoint landing in future space missions.
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-07222009-105511 |
| Date | 23 July 2009 |
| Creators | Aitken, Matthew Lawrence |
| Contributors | Andre Mazzoleni, Robert Tolson, Fred DeJarnette |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-07222009-105511/ |
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