Frequency of ADS-B Equipped Manned Aircraft Observed by the OpenSky Network

Weinert, Andrew; Brittain, Marc; Guendel, Randal

doi:10.3390/proceedings2020059015

Cited by 1 publication

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To identify potential biases in the training data, we characterized the different datasets, leveraging software and a methodology previously described in [41]. This characterization was also important in assessing if the different training datasets were similar or different and if there was a sufficient difference between datasets to warrant training a model with each dataset.…”

Section: Appendix A3 Altitude and Airspace Class Characterizationmentioning

confidence: 99%

“…For each dataset, the altitude distributions given airspace class was calculated using all latitude, longitude, and barometric altitude reports using the workflow described in [39]. Unlike [41], we did not consider the number of seats onboard the aircraft, as we lacked the metadata in the terminal area radar dataset required for that characterization. Figure A1 is for the terminal area radar dataset, Figure A2 for the OpenSky Network aerodromes dataset, and for comparison, Figure A3 is the distribution of the "Mondays" dataset used for the recent uncorrelated encounter models.…”

Section: Appendix A3 Altitude and Airspace Class Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

et al. 2022

Self Cite

View full text Add to dashboard Cite

The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations where an aircraft landing or taking off via a straight trajectory encounters another aircraft landing or taking off, or transiting by any means. The model shares the Bayesian network foundation of other Massachusetts Institute of Technology Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.

show abstract

Section: Appendix A3 Altitude and Airspace Class Characterizationmentioning

confidence: 99%

Section: Appendix A3 Altitude and Airspace Class Characterizationmentioning

confidence: 99%