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ICNS > 2022 > Best Paper Awards for ICNS 2022

Best Paper Awards for ICNS 2022

Posted: April 12, 2022 | Last Modified: April 22, 2022

Best Professional Papers

Track 1

Probabilistic Operational Volumes to Enable Risk-Based Strategic Deconfliction In Upper Class E

Peter Kuzminski, William Baden, Erin Catlett, Joseph Hopper, Robert Kluttz; The MITRE Corporation – Center for Advanced Aviation System Development, McLean, Virginia

Track 2

Artificial Intelligence for Unidentified Mode S Registers Decoding

Jaime López-Araquistain, Emilien Robert & Javier Ceballos Gutiérrez, EUROCONTROL Communication Navigation and Surveillance (CNS) Unit, Brussels, Belgium; Erwan Guillot, Thales Belgium, Tubize, Belgium

Track 3

Improvements in Operational Efficiency at Airports Using LTE Networks for Communications

Wolfgang Kampichler, Frequentis AG, Vienna, Austria; Dieter Eier, Frequentis USA Inc., Columbia, MD; Fidel Liberal, University of the Basque Country (UPV/EHU), Bilbao, Spain

Track 4

Systematic Evaluation of Cybersecurity Risks in the Urban Air Mobility Operational Environment

Addam Jordan, CNA Corporation, Arlington, VA; Katarzyna (Kasia) Jaskowska, CNA Corporation, Arlington, VA; Adam Monsalve, CNA Corporation, Arlington, VA; Rebekah Yang, CNA Corporation, Arlington, VA; Marina Rozenblat, CNA Corporation, Arlington, VA; Kenneth Freeman, NASA, Mountain View, CA; Steven Garcia, Intrinsyx Technologies Corporation, Los Altos, CA

Track 5

Update Interval Performance and Outlier Exclusion Methods for Aircraft Surveillance Systems

Joseph Canlas, John Dolan, and Dr. Michael A. Garcia, Aireon, McLean, VA

Track 6

A Preliminary Study on UAS Vertical NSE Analysis in Urban-Like Environments

Chao Deng and Chung-Hung John Wang, Air Traffic Management Research Institute, Singapore, Nanyang Technological University, Singapore 637460, Singapore; Kin Huat Low, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore

Best Student Papers

1st Place

A Chance-Constrained Optimization Approach for Air Traffic Flow Management under Capacity Uncertainty (#143)

Fadil Abdelghani, Kaiquan Cai, Minghua Zhang School of Electronics and Information Engineering, Beihang University School of Electronics and Information Engineering, Beihang University, Beijing, P. R. China

Airport capacity limitations remain a major problem for air traffic management,. However, in the presence of capacity uncertainties, the ATFM operations may be impractical or ineffective when adopting the deterministic models since the latter assumes that airport capacity is known. In this paper, we propose a new approach based on Chance Constrained Optimization Program (C-COP) taking into account airport capacity uncertainty to solve the Airport Network System Optimization (ANSO) problem.

Runner Up

Impact of Airspace Restrictions on Urban Air Mobility Commuter Demand Potential (#119)

Mihir Rimjha, Susan Hotle, Antonio Trani, Nicholas Hinze, Virginia Tech, Blackburg, Virginia, USA

This study aims to understand the impact of airspace restrictions on Urban Air Mobility (UAM) commuter demand potential in the New York City region. The potential for UAM is higher in congested cities with substantial commuter populations, but often these cities are served by one or more airports with congested airspaces encompassing over a large part of the urban area. The integration between commercial airspaces and future UAM airspace is among the major challenges to overcome. This study analyzes UAM demand potential with three scenarios of airspace restrictions- No Restrictions, Class-B restrictions only, Class-B/D restrictions.

2nd Runner Up

A Review of Kalman Filter with Artificial Intelligence Techniques (#144)

Sukkeun Kim, Ivan Petrunin, Hyo-Sang Shin, School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield, U.K.

The Kalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called “black box”. In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance.

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Posted Under: 2022

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