Multi-sensor fusion based state estimation for UAV
Unmanned Aerial Vehicle (UAV) is a device capable of flying in the air. It is very popular in a wide range of industries and it is capable of carrying out different tasks. State estimation is required for autonomous operations of UAVs. There are several methods for state estimation, with sensor fusi...
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2020
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sg-ntu-dr.10356-1416182023-07-07T18:50:16Z Multi-sensor fusion based state estimation for UAV Tan, Edwin Yu Jie Xie Lihua School of Electrical and Electronic Engineering ELHXIE@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Unmanned Aerial Vehicle (UAV) is a device capable of flying in the air. It is very popular in a wide range of industries and it is capable of carrying out different tasks. State estimation is required for autonomous operations of UAVs. There are several methods for state estimation, with sensor fusion based state estimation being one of them. One of the uses of state estimation is for UAV localisation. This paper presents a sensor fusion based state estimation using Extended Kalman Filter (EKF) algorithm for localisation of a UAV. Based on the distance measurements, IMU data and GPS data from the quadcopter, the EKF is used for state estimation and is implemented to obtain the estimated position of the quadcopter. Simulation results shows that Global Positioning System (GPS) and Inertial Measurement Unit (IMU) fusion is able to provide a precise and reliable localisation. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-09T08:24:28Z 2020-06-09T08:24:28Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141618 en A1242-191 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Computer hardware, software and systems Tan, Edwin Yu Jie Multi-sensor fusion based state estimation for UAV |
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Unmanned Aerial Vehicle (UAV) is a device capable of flying in the air. It is very popular in a wide range of industries and it is capable of carrying out different tasks. State estimation is required for autonomous operations of UAVs. There are several methods for state estimation, with sensor fusion based state estimation being one of them. One of the uses of state estimation is for UAV localisation. This paper presents a sensor fusion based state estimation using Extended Kalman Filter (EKF) algorithm for localisation of a UAV. Based on the distance measurements, IMU data and GPS data from the quadcopter, the EKF is used for state estimation and is implemented to obtain the estimated position of the quadcopter. Simulation results shows that Global Positioning System (GPS) and Inertial Measurement Unit (IMU) fusion is able to provide a precise and reliable localisation. |
author2 |
Xie Lihua |
author_facet |
Xie Lihua Tan, Edwin Yu Jie |
format |
Final Year Project |
author |
Tan, Edwin Yu Jie |
author_sort |
Tan, Edwin Yu Jie |
title |
Multi-sensor fusion based state estimation for UAV |
title_short |
Multi-sensor fusion based state estimation for UAV |
title_full |
Multi-sensor fusion based state estimation for UAV |
title_fullStr |
Multi-sensor fusion based state estimation for UAV |
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Multi-sensor fusion based state estimation for UAV |
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multi-sensor fusion based state estimation for uav |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/141618 |
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1772825260798771200 |