Due Regard Encounter Model Version 1.0

Abstract

Airspace encounter models describe encounter situations that may occur between aircraft in the airspace and are a critical component of safety assessment of sense and avoid (SAA) systems for Unmanned Aircraft Systems (UASs). Some UAS will fly in international airspace under due regard and may encounter other aircraft during these operations. In these types of encounters, the intruder aircraft is likely receiving air traffic control (ATC) services, but the UAS is not. Thus, there is a need for a due regard encounter model that can be used to generate these types of encounters. This report describes the development of a due regard encounter model. In order to build the model, Lincoln Laboratory collected data for aircraft flying in international airspace using the Enhanced Traffic Management System (ETMS) data feed that was provided by the Volpe Center. Lincoln processed these data, and extracted important features to construct the model. The model is based on Bayesian networks that represent the probabilistic relationship between variables that describe how aircraft behave. The model is used to construct random aircraft trajectories that are statistically similar to those observed in the airspace. A large collection of encounters generated from an airspace encounter model can be used to evaluate the performance of a SAA system against encounter situations representative of those expected to actually occur in the airspace.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 19, 2013
Accession Number
ADA589692

Entities

People

  • Andrew Weinert
  • J. D. Griffith
  • Matthew W. Edwards
  • Raymond M. Miraflor

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Traffic
  • Aircrafts
  • Bayesian Networks
  • Collision Avoidance Systems
  • Computational Science
  • Data Science
  • Geographic Regions
  • Information Science
  • Models
  • Probability
  • Sense And Avoid Systems
  • Statistics
  • Trajectories
  • United States
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles

Readers

  • Computational Modeling and Simulation
  • Sensor Fusion and Tracking Systems.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Space