東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Observability-enhanced dual-filter d...

Searcy, Jason David.

Linked to FindBook

Google Book

Amazon

博客來

Observability-enhanced dual-filter design for attitude estimation with minimum observations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Observability-enhanced dual-filter design for attitude estimation with minimum observations./
Author:	Searcy, Jason David.
Description:	156 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
Contained By:	Dissertation Abstracts International75-06B(E).
Subject:	Engineering, Aerospace. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3612670
ISBN:	9781303744242

Observability-enhanced dual-filter design for attitude estimation with minimum observations.
Searcy, Jason David.

Observability-enhanced dual-filter design for attitude estimation with minimum observations. - 156 p.

Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2013.

Determining spacecraft attitude in real time using only magnetometer data presents a challenging filtering problem. A flexible and computationally efficient method for solving the spacecraft attitude using only an inexpensive and reliable magnetometer would be a useful option for satellite missions, particularly those with modest budgets. The primary challenge is that magnetometers only instantaneously resolve two axes of the spacecraft attitude. Typically, magnetometers are used in conjunction with other sensors to resolve all three axes. However, by using a filter over an adequately long orbit arc, the magnetometer data can yield full attitude, and in near real time. The method presented solves the problem using a two-nested extended Kalman filter as a means to improve convergence. In the first filter, the magnetic field data are filtered to obtain the magnetic field derivative vector, which is combined with the magnetic field vector in the second filter to fully resolve the attitude. As revealed by a literature review and previous research by the author, this method fails to accurately estimate the attitude unless the spacecraft is spin-stabilized with a relatively high angular velocity. To address this limiting restriction, the observability of the problem is examined from an analytical perspective. This study separates the problem into two stages and considers different methods for solving each stage. The first estimates the magnetic field derivative and possibly the angular rates, and then uses this information to calculate the attitude in the second stage. A new dynamic model is developed to estimate angular rates without estimating the attitude quaternion. MATLAB numerical routines are used to solve the complex nonlinear system of equations to yield a deterministic method. Finally, a parametric study analyzes the accuracy and utility of this method for different orbit trajectories and angular rates.

ISBN: 9781303744242Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Observability-enhanced dual-filter design for attitude estimation with minimum observations.
LDR:02865nmm a2200277 4500 001 2055450
005 20150127125438.5
008 170521s2013 ||||||||||||||||| ||eng d
020 $a 9781303744242
035 $a (MiAaPQ)AAI3612670
035 $a AAI3612670
040 $a MiAaPQ $c MiAaPQ
100 1 $a Searcy, Jason David. $3 3169111
245 1 0 $a Observability-enhanced dual-filter design for attitude estimation with minimum observations.
300 $a 156 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
500 $a Adviser: Henry J. Pernicka.
502 $a Thesis (Ph.D.)--Missouri University of Science and Technology, 2013.
520 $a Determining spacecraft attitude in real time using only magnetometer data presents a challenging filtering problem. A flexible and computationally efficient method for solving the spacecraft attitude using only an inexpensive and reliable magnetometer would be a useful option for satellite missions, particularly those with modest budgets. The primary challenge is that magnetometers only instantaneously resolve two axes of the spacecraft attitude. Typically, magnetometers are used in conjunction with other sensors to resolve all three axes. However, by using a filter over an adequately long orbit arc, the magnetometer data can yield full attitude, and in near real time. The method presented solves the problem using a two-nested extended Kalman filter as a means to improve convergence. In the first filter, the magnetic field data are filtered to obtain the magnetic field derivative vector, which is combined with the magnetic field vector in the second filter to fully resolve the attitude. As revealed by a literature review and previous research by the author, this method fails to accurately estimate the attitude unless the spacecraft is spin-stabilized with a relatively high angular velocity. To address this limiting restriction, the observability of the problem is examined from an analytical perspective. This study separates the problem into two stages and considers different methods for solving each stage. The first estimates the magnetic field derivative and possibly the angular rates, and then uses this information to calculate the attitude in the second stage. A new dynamic model is developed to estimate angular rates without estimating the attitude quaternion. MATLAB numerical routines are used to solve the complex nonlinear system of equations to yield a deterministic method. Finally, a parametric study analyzes the accuracy and utility of this method for different orbit trajectories and angular rates.
590 $a School code: 0587.
650 4 $a Engineering, Aerospace. $3 1018395
650 4 $a Engineering, General. $3 1020744
690 $a 0538
690 $a 0537
710 2 $a Missouri University of Science and Technology. $b Aerospace Engineering. $3 3169112
773 0 $t Dissertation Abstracts International $g 75-06B(E).
790 $a 0587
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3612670