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GPS denied navigation using low-cost inertial sensors and recurrent neural networks / Ahmed Ali Ahmed Abdulmajuid ; Supervised Gamal M. Elbayoumi , Osama S. Mohammady , Mohannad A. Draz

By: Contributor(s): Material type: ScoreScoreLanguage: English Publication details: Cairo : Ahmed Ali Ahmed Abdulmajuid , 2021Description: 99 P. : charts ; 30cmOther title:
  • الملاحة فى غ{u٠٦أأ}اب نظام التموضع العالمى باستخدام مستشعرات القصور الذاتى منخفضة التكلفة والشبكات العصب{u٠٦أأ}ة المتكررة [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Aerospace Engineering Summary: Autonomous missions of drones require continuous and reliable estimates for their velocity and position. Traditionally, Extended Kalman Filtering (EKF) is applied to measurements from Gyroscope, Accelerometer, Magnetometer, Barometer and GPS to produce these estimates. When the GPS signal is lost, estimates deteriorate and become unusable in a few seconds, especially when using low-cost inertial sensors. This thesis proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable state estimates in the absence of GPS signal. On average, EKF positioning error grows to around 40 kilometers in five minutes of GPS-less typical drone flight.The proposed method reduces that error by 98% in the same GPS outage
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.01.M.Sc.2021.Ah.G (Browse shelf(Opens below)) Not for loan 01010110085041000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.01.M.Sc.2021.Ah.G (Browse shelf(Opens below)) 85041.CD Not for loan 01020110085041000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Aerospace Engineering

Autonomous missions of drones require continuous and reliable estimates for their velocity and position. Traditionally, Extended Kalman Filtering (EKF) is applied to measurements from Gyroscope, Accelerometer, Magnetometer, Barometer and GPS to produce these estimates. When the GPS signal is lost, estimates deteriorate and become unusable in a few seconds, especially when using low-cost inertial sensors. This thesis proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable state estimates in the absence of GPS signal. On average, EKF positioning error grows to around 40 kilometers in five minutes of GPS-less typical drone flight.The proposed method reduces that error by 98% in the same GPS outage

Issued also as CD

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