Quantifying AAM Communications Quality using Machine Learning

被引：1

作者：

Wieland, Frederick ^{[1
]}

Matolak, David ^{[2
]}

Drescher, Zack ^{[1
]}

机构：

[1] Mosaic ATM, Leesburg, VA 20176 USA

[2] Univ South Carolina, Elect Engn, Columbia, SC 29208 USA

来源：

2023 INTEGRATED COMMUNICATION, NAVIGATION AND SURVEILLANCE CONFERENCE, ICNS | 2023年

关键词：

Air-Ground Communications; Advanced Air Mobility (AAM); Machine Learning; Wireless Transmission; CHANNEL;

D O I：

10.1109/ICNS58246.2023.10124258

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Achieving Advanced Air Mobility (AAM) on a scale envisioned by industry proponents and other stakeholders will require an Air-Ground Communication (AG Comm) system that is robust and resilient to failures. In this paper we describe a Machine Learning-based tool that quickly predicts communication path loss for AAM flights, a key metric for establishing and maintaining robust AG Comm. We have implemented this tool and tested it using both simulated scenarios and live flight data. This paper describes the tool itself and the results obtained comparing it with "ground truth" as established through physics-based ray-tracing computations.

引用

页数：8

共 10 条

[1]

Deloitte, 2021, ADV AIR MOB CAN US A

[2]

Federal Aviation Administration, 2020, UNMANNED AERIAL SYST

[3]

Goodrich K. H., 2021, AIAA SCITECH 2021 FO

[4] Detection, Tracking, and Interdiction for Amateur Drones [J].