A high-precision unmanned aerial vehicle positioning system based on ultra-wideband technology

2021 ◽  
Author(s):  
Tianyu Li ◽  
Fa-jie Duan ◽  
Chunjiang Liang ◽  
jiajia Jiang ◽  
Xiao Fu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Precision ◽  
Ultra Wideband ◽  
Positioning System ◽  
Aerial Vehicle ◽  
Vehicle Positioning

Development of the navigation complex structure for a high-precision unmanned aerial vehicle for flight in the atmosphere

2020 ◽  
Keyword(s):  
Mathematical Modeling ◽  
Unmanned Aerial Vehicle ◽  
High Precision ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Complex Structure ◽  
Navigation Systems ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
System Errors

The navigation systems as part of the navigation complex of a high-precision unmanned aerial vehicle in conditions of different altitude flight are investigated. The working contours of the navigation complex with correction algorithms for an unmanned aerial vehicle during high-altitude and low-altitude flights are formed. Mathematical models of inertial navigation system errors used in non-linear and linear Kalman filters are presented. The results of mathematical modeling demonstrate the effectiveness of the working contours effectiveness of the navigation complex with correction algorithms. Keywords high-precision unmanned aerial vehicle; navigation complex; multi-altitude flight; work circuit; passive noises; Kalman filter; correction

scholarly journals A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878175 ◽  
Author(s):  
Shahrukh Ashraf ◽  
Priyanka Aggarwal ◽  
Praveen Damacharla ◽  
Hong Wang ◽  
Ahmad Y Javaid ◽  
...  
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Motion Vector ◽  
Block Matching ◽  
Positioning System ◽  
Simulation Studies ◽  
Flow Measurements ◽  
Global Positioning ◽  
Aerial Vehicle

The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow–based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system–denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony–based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of ~95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.

scholarly journals Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

2017 ◽  
Vol 9 (3) ◽  
pp. 169-186 ◽  
Author(s):  
Kexin Guo ◽  
Zhirong Qiu ◽  
Wei Meng ◽  
Lihua Xie ◽  
Rodney Teo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Ultra Wideband ◽  
Cooperative Localization ◽  
Relative Localization ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

scholarly journals Penentuan Lintasan Pergerakan Quadcopter Berbasis GPS (Global Positioning System)

2019 ◽  
Vol 1 (2) ◽  
pp. 1-14
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hadi Supriyanto
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Positioning System ◽  
Global Positioning ◽  
Aerial Vehicle ◽  
Rotary Wing

UNMANNED AERIAL VEHICLE (UAV) merupakan sebuah kendaraan udara tanpa awak yang dapat dikendalikan. Terdapat dua tipe UAV, yakni fixed wing dan rotary wing. Quadcopter menjadi salah satu tipe UAV rotary wing yang banyak digunakan dalam berbagai kebutuhan, seperti eksplorasi dan pengambilan citra. Pada penelitian ini Quadcopter berfungsi sebagai kendaraan yang harus bergerak mengikuti lintasan, dimana lintasan yang dikuti oleh Quadcopter berasal dari GPS yang dihasilkan oleh objek yang diikuti (Modul Utama). Tipe GPS yang terpasang pada Quadcopter (GPS1) maupun pada Modul Utama (GPS2) adalah  GPS Ublox NEO. Prinsip kerja sistem adalah quadcopter mengikuti Koordinat-koordinat lintasan yang dihasilkan oleh GPS1, di mana data-data lintasan GPS1 dikirim ke Quadcopter menggunakan media Bluetooth.  Dalam pergerakannya, Quadcopter akan terus-menerus membandingkan data-data koordinat yang dihasikan posisi Quadcopter dengan data-data koordinat lintasan yang sudah diterima. Pengujian pada Receiver GPS Modul Utama (GPS1) dan Receiver GPS Quadcoter (GPS2), kedua GPS mampu mendapatkan data GPS dari satelit.  Kesalahan/perbedaan data dari GPS1 dan GPS2  pada pengujian pergerakkan Quadcopter  untuk mengikuti  Modul Utama sebagai titik tujuan sebesar 53% pada garis lintang dan 51% pada garis bujur.

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