Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods

With the increasing availability of ADS-B transponders on commercial aircraft, as well as the rapidly growing deployment of ground stations that provide public access to their data, accessing open aircraft flight data is becoming easier for researchers. Given the large number of operational aircraft, significant amounts of flight data can be...

Modeling and Inferring Aircraft Takeoff Mass from Runway ADS-B Data

Aircraft mass is an important parameter in many ways, either to build aircraft performance models, to predict flight trajectories, or to simulate air traffic. Mass data is usually considered as sensitive information for airlines and is, therefore, not disclosed to researchers publicly. In this paper, we use two methods to infer the mass of an...

Modeling aircraft performance parameters with open ADS-B data

Open access to flight data from ADS-B (Automatic Dependent Surveillance Broadcast) has provided researchers more insights for air traffic management than aircraft tracking alone. With large quantities of trajectory data collected from a wide range of different aircraft types, it is possible to extract accurate aircraft performance parameters....

Large-Scale ADS-B Data and Signal Quality Analysis

To investigate the contradicting findings of previous studies that investigated ADS-B quality, a study was per-formed to analyze the data and signal quality of ADS-B. For this study, a large dataset of raw ADS-B messages was analyzed, regarding the quality of the data and the signal, differentiating between internal and external sources of...

Bayesian Inference of Aircraft Initial Mass

Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other ATM topics. However, it is a common challenge for researchers who have no access to this proprietary information. Previously, several studies have proposed methods to estimates aircraft weight, most of which are focused on...

Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model

Wind is an important parameter in many air traffic management researches, as it often introduces significant uncertainties in aircraft performance studies and trajectory predictions. Obtaining accurate wind field information has always been a challenge due to the availability of weather sensors. Traditionally, there is no direct method to...

Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method

This paper focuses on estimating aircraft mass and thrust setting using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using solely ADS-B and Mode-S data, flight states such as position, velocity, and wind speed are collected and...

Aircraft Drag Polar Estimation Based on a Stochastic Hierarchical Model

The aerodynamic properties of an aircraft determine a crucial part of the aircraft performance model. Deriving accurate aerodynamic coefficients requires detailed knowledge of the aircraft’s design. These designs and parameters are well protected by aircraft manufacturers. They rarely can be used in public research. Very detailed aerodynamic...

Reconstructing Aircraft Turn Manoeuvres for Trajectory Analyses Using ADS-B Data

The Automatic Dependent Surveillance-Broadcast (ADS-B) data has become one of the most popular sources of data for trajectory-based ATM studies. It is can be received in most of the world without restrictions. Extended coverage can be achieved with a network of low-cost receivers and satellites. However, the fact that ADS-B is designed to...