Unscented Kalman filter-based method for spacecraft navigation using resident space objects
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ORIGINAL PAPER
Unscented Kalman filter-based method for spacecraft navigation using resident space objects Matthew Driedger1
· Michael Rososhansky1 · Philip Ferguson1
Received: 27 April 2020 / Revised: 6 July 2020 / Accepted: 20 July 2020 / Published online: 18 August 2020 © Shanghai Jiao Tong University 2020
Abstract The number of resident space objects (RSOs) in orbit has increased dramatically within the last 10 years. While RSOs pose a serious challenge to our continued use of space, these objects also provide an opportunity to improve on-orbit state estimation by detecting and identifying these objects using star trackers. While star trackers are currently used to determine the orientation of their host spacecraft, their utility could be expanded to both position and orientation estimation by harnessing RSO detections. In order for RSO-based optical navigation to be commercially viable, a reliable filter covariance estimate is required. This paper introduces an Unscented Kalman Filter (UKF) for estimating an observing spacecraft’s position and attitude based on RSO observations. To ensure that this filter is reliable, a new assessment technique is introduced where we compare the moving standard deviation and mean of the filter’s estimate error with the predicted error based on the filter’s covariance estimate. We demonstrate the utility of this assessment method by comparing the covariance trusts for our UKF and a previously developed Extended Kalman Filter. Keywords RSO-based optical navigation · Unscented Kalman Filter · Optical navigation · Spacecraft navigation · Guidance, navigation, and control
1 Introduction This research explores how an Unscented Kalman Filter (UKF) using optical detections of Resident Space Objects (RSOs) by star trackers can enable these sensors to perform full-state estimation. By extracting more information from star trackers, satellite designers may be able to improve their spacecraft’s attitude and position estimates while simultaneously reducing the cadre of sensors required to complete a given mission and reducing the spacecraft’s reliance on ground-based tracking assets. Star trackers take images of stars and compare these to an on-board star catalogue to support high-accuracy orientation determination, usually better than 10 arcseconds [5]. However, star trackers have had to become increasingly intelligent
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Matthew Driedger [email protected] Philip Ferguson [email protected]
1
Department of Mechanical Engineering, University of Manitoba, E1-451 75A Chancellor’s Circle, Winnipeg MB R3T 5V6, Canada
to reject false star images arising from glinting space objects such as other satellites, spent rocket bodies, and debris [5]. As space commerce continues to grow, so does the density of these objects, known as Resident Space Objects (RSOs). RSOs are especially abundant in popular orbits such as low earth orbit, polar orbits, sun synchronous orbits, and geostationary orbits [17]. To compensate for the increased density of resident space objects, the star tra
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