國立虎尾科技大學 |

Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs during GPS Denied Applications.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs during GPS Denied Applications./
作者:	Seliquinii, Sky.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	159 p.
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Aerospace engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29206423
ISBN:	9798819394113

Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs during GPS Denied Applications.
Seliquinii, Sky.

Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs during GPS Denied Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 159 p.

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--Old Dominion University, 2022.

This item must not be sold to any third party vendors.

Unmanned Aerial Vehicles (UAVs) are widely used across the industry and have a strong military application for defense. As UAVs become more accessible so does the increase of their applications, now being more limited by one’s imagination as opposed to the past where micro electric components were the limiting factor. Almost all of the applications require GPS or radio guidance. For more covert and longer range missions relying solely on GPS and radio is insufficient as the Unmanned Aerial System is vulnerable to malicious encounters like GPS Jamming and GPS Spoofing. For long range mission GPS denied envi- ronments are common where loss of signal is experienced. For autonomous flight GPS is a fundamental requirement. In this work an advanced inertial navigation system is proposed along with a programmable Pixhawk flight controller and Cube Black autopilot. A Raspberry Pi serves as a companion computer running autonomous flight missions and providing data acquisition. The advancement in inertial navigation comes from the implementation of a high end Analog Devices’ IMU providing input to an Extended Kalman Filter (EKF) to reduce error associated with measurement noise. The EKF is a efficient recursive computation applying the least-squares method. UAS flight controller simulations and calibrations were conducted to ensure the expected flight capabilities were achieved. The developed soft- ware and hardware was implemented in a Quadcopter build to perform flight test. Flight test data were used to analyze the performance post flight. Later, simulated feedback of the inertial navigation based state estimates (from flight test data) is performed to ensure reliable position data during GPS denied flight. The EKF applied to perform strapdown navigation was a limited success at estimating the vehicles’ inertial states but only when tuned for the specific flight trajectory. The predicted position was succesfully converted to GPS data and passed to the autopilot in a LINUX based simulations ensuring autonmous mission capability is maintainable in GPS denied enviornments. The results from this re- search can be applied with ease to any vehicle operating with a Pixhawk controller and a companion computer of the appropriate processing capability.

ISBN: 9798819394113Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

Extended Kalman filter

Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs during GPS Denied Applications.
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300 $a 159 p.
500 $a Source: Masters Abstracts International, Volume: 83-12.
500 $a Advisor: Alberts, Thomas.
502 $a Thesis (M.S.)--Old Dominion University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Unmanned Aerial Vehicles (UAVs) are widely used across the industry and have a strong military application for defense. As UAVs become more accessible so does the increase of their applications, now being more limited by one’s imagination as opposed to the past where micro electric components were the limiting factor. Almost all of the applications require GPS or radio guidance. For more covert and longer range missions relying solely on GPS and radio is insufficient as the Unmanned Aerial System is vulnerable to malicious encounters like GPS Jamming and GPS Spoofing. For long range mission GPS denied envi- ronments are common where loss of signal is experienced. For autonomous flight GPS is a fundamental requirement. In this work an advanced inertial navigation system is proposed along with a programmable Pixhawk flight controller and Cube Black autopilot. A Raspberry Pi serves as a companion computer running autonomous flight missions and providing data acquisition. The advancement in inertial navigation comes from the implementation of a high end Analog Devices’ IMU providing input to an Extended Kalman Filter (EKF) to reduce error associated with measurement noise. The EKF is a efficient recursive computation applying the least-squares method. UAS flight controller simulations and calibrations were conducted to ensure the expected flight capabilities were achieved. The developed soft- ware and hardware was implemented in a Quadcopter build to perform flight test. Flight test data were used to analyze the performance post flight. Later, simulated feedback of the inertial navigation based state estimates (from flight test data) is performed to ensure reliable position data during GPS denied flight. The EKF applied to perform strapdown navigation was a limited success at estimating the vehicles’ inertial states but only when tuned for the specific flight trajectory. The predicted position was succesfully converted to GPS data and passed to the autopilot in a LINUX based simulations ensuring autonmous mission capability is maintainable in GPS denied enviornments. The results from this re- search can be applied with ease to any vehicle operating with a Pixhawk controller and a companion computer of the appropriate processing capability.
590 $a School code: 0418.
650 4 $a Aerospace engineering. $3 686400
653 $a Extended Kalman filter
653 $a Inertial navigation
653 $a Global positioning systems
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710 2 $a Old Dominion University. $b Mechanical & Aerospace Engineering. $3 1178891
773 0 $t Masters Abstracts International $g 83-12.
790 $a 0418
791 $a M.S.
792 $a 2022
793 $a English
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