Tirri, Anna Elena (2014) Exploiting innovative sensor data fusion techniques for Sense and Avoid units to be installed on-board Unmanned Aerial Systems. [Tesi di dottorato]


Download (2MB) | Preview
[error in script] [error in script]
Item Type: Tesi di dottorato
Lingua: English
Title: Exploiting innovative sensor data fusion techniques for Sense and Avoid units to be installed on-board Unmanned Aerial Systems
Tirri, Anna Elenaannaelena.tirri@unina.it
Date: 2014
Number of Pages: 110
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Industriale
Scuola di dottorato: Ingegneria industriale
Dottorato: Ingegneria aerospaziale, navale e della qualità
Ciclo di dottorato: 26
Coordinatore del Corso di dottorato:
Moccia, AntonioUNSPECIFIED
Accardo, DomenicoUNSPECIFIED
Date: 2014
Number of Pages: 110
Uncontrolled Keywords: Unmanned Aerial System, Sense and Avoid, Particle Filter
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-IND/05 - Impianti e sistemi aerospaziali
Date Deposited: 07 Apr 2014 12:52
Last Modified: 26 Jan 2015 11:54
URI: http://www.fedoa.unina.it/id/eprint/9904


Sense and Avoid Systems play an important role on-board Unmanned Aerial Vehicles in order to be allowed flying into civil Airspace. The key idea of these systems is to detect obstacles in the own trajectories, tracking the detected objects and execute a collision avoidance manoeuvre if the obstacle is closely approaching, thus becoming a collision threat. These functions can be achieved defining an adequate sensor setup, choosing a dynamic model that allows describing the target motion properly, identifying a suitable filtering methodologies given the non-linearities in the dynamic model. This thesis deals with identification and test of innovative sensor data fusion techniques to be implemented in a fully autonomous system devoted to avoidance of non-cooperative intruders. In particular, sensors, hardware and software architectures are described, focusing the attention on the impact of an innovative filtering methodology, such as Particle Filter, on the performance of the developed tracking software with respect to assessed technique, such as Extended Kalman Filter. The Particle Filter Obstacle Detect and Tracking system has been developed and tested in off-line simulations based on real data gathered during a flight test campaign within TECVOL project (carried out in collaboration with the Italian Aerospace Research Center). In order to evaluate the effectiveness of the developed software for the assessment of a collision risk, an analysis has been carried out for the estimation of the Distance at Closest Point of Approach. Numerical results have shown that the Particle Filter algorithm is able to provide performance comparable to the Extended Kalman Filter ones and allows obtaining some improvements with respect to the EKF in terms of DCPA, thus reducing the delay in the collision detection.


Downloads per month over past year

Actions (login required)

View Item View Item