Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter

Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often the...

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Main Author: McFerrin, Melinda Ruth
Format: Others
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/2152/ETD-UT-2009-08-365
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2009-08-3652015-09-20T16:53:47ZOptical navigation: comparison of the extended Kalman filter and the unscented Kalman filterMcFerrin, Melinda RuthExtended Kalman FilterUnscented Kalman FilterSpacecraft NavigationSmall satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust. The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites. The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude. The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time.text2010-06-04T14:44:13Z2010-06-04T14:44:13Z2009-082010-06-04T14:44:13ZAugust 2009thesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2009-08-365eng
collection NDLTD
language English
format Others
sources NDLTD
topic Extended Kalman Filter
Unscented Kalman Filter
Spacecraft Navigation
spellingShingle Extended Kalman Filter
Unscented Kalman Filter
Spacecraft Navigation
McFerrin, Melinda Ruth
Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
description Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust. The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites. The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude. The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time. === text
author McFerrin, Melinda Ruth
author_facet McFerrin, Melinda Ruth
author_sort McFerrin, Melinda Ruth
title Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
title_short Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
title_full Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
title_fullStr Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
title_full_unstemmed Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
title_sort optical navigation: comparison of the extended kalman filter and the unscented kalman filter
publishDate 2010
url http://hdl.handle.net/2152/ETD-UT-2009-08-365
work_keys_str_mv AT mcferrinmelindaruth opticalnavigationcomparisonoftheextendedkalmanfilterandtheunscentedkalmanfilter
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