Low-cost sensors data fusion for small size unmanned aerial vehicles navigation and guidance

Sabatini, R, Ramasamy, S, Gardi, A and Salazar, R 2013, 'Low-cost sensors data fusion for small size unmanned aerial vehicles navigation and guidance', International Journal of Unmanned Systems Engineering, vol. 1, no. 3, pp. 16-47.


Document type: Journal Article
Collection: Journal Articles

Attached Files
Name Description MIMEType Size
n2006042910.pdf Published Version application/pdf 2.39MB
Title Low-cost sensors data fusion for small size unmanned aerial vehicles navigation and guidance
Author(s) Sabatini, R
Ramasamy, S
Gardi, A
Salazar, R
Year 2013
Journal name International Journal of Unmanned Systems Engineering
Volume number 1
Issue number 3
Start page 16
End page 47
Total pages 32
Publisher Marques Aviation
Abstract A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.
Subject Avionics
Navigation and Position Fixing
Control Systems, Robotics and Automation
Engineering Instrumentation
Keyword(s) Unmanned aerial vehicle
Vision based navigation
MEMS inertial measurement unit
GNSS
GNSS attitude determination
Low-cost navigation sensors and EKF
DOI - identifier 10.14323/ijuseng.2013.11
Copyright notice © Marques Aviation Ltd.
ISSN 2052-112X
Versions
Version Filter Type
Altmetric details:
Access Statistics: 327 Abstract Views, 88 File Downloads  -  Detailed Statistics
Created: Wed, 12 Mar 2014, 08:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us