The research activity is developed at the Department of Aerospace Engineering of the University of Naples "Federico II". It is funded by HALA! Research Network.
The broad aim of the research is developing a software architecture that can generate robust and reliable estimates of the collision threat level for near range encounters. In order to solve the complex problem, specific advanced data fusion and decision making issues could be exploited. In particular, the innovative aspects of the research are based on a definition of an end-to-end strategy from raw sensor data to the evaluation of the collision threat, the development of an adaptive strategy that can be used with any type of sensor with different performance levels and the adoption of state of art sensor data fusion and decision making solutions, such as Unscented and Particle Filtering, Fuzzy Logic, Neural Networks, Probabilistic Risk Assessment, and Interval Analysis.
In order to achieve the PhD research objectives, the following approach is adopted for the investigation:
• Study to determine all possible host platforms and sensor suites specifications;
• Analysis of raw sensor data pre-processing routines that must be developed to generate data that have uniform format for the following processing steps;
• Design of the mainframe multi-modal software architecture flow-chart that generates collision threat estimates;
• Definition of partial/total validation procedure to be used at numerical, flight, and mixed mode level testing, e.g. with indoor hardware in the loop tests;
• Development of each block that is included in the proposed software architecture using a Mathworks Matlab and/or a C++/Java compiler, with the possibility also to compile a partial segment of software for real time applications on an on-board CPU.
3. Implication for research/Expected Results
The growing interest in Unmanned Aerial System introduction into National Airspace has required certifications and standards to allow the UAS to fly in civilian, non‐segregated airspace. Following the most important guidelines, the onboard avionics of these aircraft shall include a Sense and Avoid system that is capable to replace the human pilot in performing visual collision threat detection and avoidance. Therefore, several experiences have been carried out worldwide in order to develop a system that can perform the above‐mentioned function but the all reported solutions are customized for a specific platform and/or application. Thus, the main expected finding of the proposed research is the derivation of a standard data processing scheme that will be used to implement Autonomous Obstacle Sensing and Collision Detection functions onboard several types of platforms that can be equipped with different sensor suites. This results can be accomplished by exploiting advanced Data Fusion and Decision Making approach.
"EUROCONTROL Specifications for The Use of Military Unmanned Aerial Vehicles as Operational Air Traffic Outside Segregated Airspace", EUROCONTROL, doc no. SPEC‐0102, v.1.0, Bruxelles, Belgium, July 27th, 2007.
"Airworthiness Certification of Unmanned Aircraft Systems," US Federal Aviation Administration (FAA), Order 8130.34, Washington DC, USA, March 27th, 2008.
Fasano, G., Accardo, D., Moccia, A., Carbone, C., Ciniglio, U., Corraro, F., and Luongo, S. “Multi‐Sensor‐Based Fully Autonomous Non‐Cooperative Collision Avoidance System for Unmanned Air Vehicles,” AIAA Journal of Aerospace Computing, Information, and Communication, vol. 5, issue 10, pp. 338‐ 2008.
Fasano, G., Forlenza, L., Tirri, A.E., Accardo, D., and Moccia, A., “Multi-Sensor Data Fusion: A Tool to Enable UAS Integration into Civil Airspace,” 2011 IEEE/AIAA 31th Digital Avionics Systems Conference (DASC), Seattle WA, USA, 16-20 October 2011, pp.5C3-1.
Tirri, A.E., Fasano, G., Accardo, D., Moccia, A., “Airborne Tracking based on Particle Filtering for UAS Sense and Avoid,” AIAA Infotech@Aerospace 2012, Garden Grove, CA, USA, 19-21 giugno 2012.