Junzi Sun, Ph.D.

Open-source projects (selected) View more on GitHub

Active OpenAP: Open Aircraft Performance Model
This project aims at producing a fully open aircraft performance model. The OpenAP is about to model aircraft performance parameters involving kinematic, thrust, drag, and fuel flow. The repository contains all OpenAP databases and a Python implementation which facilitates data access and aircraft performance computation.
Active pyModeS: An Open-source Python Mode-S Decoder
PyModeS is a Python library for decoding and encoding Mode S (including ADS-B) messages. It is an open-source project that receives great support and contributions from the aviation community. this python package can be imported into existing python project, and it also be used as a standalone tool to view and save live traffic data.
Active Meteo-Particle model Python library
A Python library for wind field estimation based on the Meteo-Particle model. The wind and temperature are obtained from ADS-B and Mode S data using pyModeS.
Active The 1090Mhz Riddle: An open-access book on decoding ADS-B and Mode S
Developing a guide for decoding ADS-B and Mode-S messages. Started as the journal of developing the pyModeS library. It has been growing into a project of writing an open-access book.
Active BlueSky: Open air traffic simulator
BlueSky is the open-source Air Traffic Simulation project started by prof. Hoekstra. I am contributing to the modules related to aircraft performance, data feed, wind models, etc.
2015 - 2018 World Aircraft Database
Constructing a database to search for aircraft IDs (such as ICAO address, and registrations ID) and related information. It is built in Python / Flask and with data from Flight Radar 24.
2015 - 2019 Flight data processor
A python library to process and analyze ADS-B flight data. It can reconstruct flight trajectories from ADS-B data and identify flight phases.

Thesis research projects

Active Modeling unpredictability of flight trajectories

This study investigates different methods to tackle the unpredictability of flight trajectories caused by various random factors. The goal is to model trajectory prediction uncertainty caused by these factors during different flight phases.

Student: R. Grass
Active Data-driven trajectory prediction incorporating air traffic dynamics

This study focuses on the medium to long term predictions of flight trajectories based on data-driven approaches. We aim to to model the dynamics of air traffic situations and incorporate this model to improve flight trajectory predictions.

Student: J.L. Overkamp
Active Mining ATC radiotelephony voice communication data

This study aims at constructing an open RT corpus and an ATCo speech recognition model using neural networks and domain specific information.

Student: J. Lubberding
Active ADS-B signal integrity and security verification

This study aims at developing a method to verify and validate ADS-B signal integrity using low-cost concurrent multi-channel software defined radio receiver.

Student: W. Huygen
Active Constructing aircraft emission model from open data

This study aims at expanding OpenAP’s capability by developing an emission model, which can calculate emissions such as CO, NOx, CO2, and H2O based on ADS-B data.

Student: J. Jongbloed
Active Data-driven Safety Indicators for Flight Operations

The study proposes a proactive risk management strategy that uses safety indicators, which are obtained from ADS-B data using various data mining techniques. These safety indicators are then used to identify anomalous safety events and precursors at Schiphol Airport.

Student: A. Bonifazi
2018 ADS-B and Mode S data to enhance aviation meteorology and aircraft performance models

This study makes use of open ADS-B and Mode S to construct accurate wind and temperature field, which can be used to improved existing aircraft kinematic performance models.

Student: Q.H. Vû
2016 Aircraft performance parameter estimation using ADS-B data

This study makes the first attempt to model flight envelop and to estimate aircraft performance parameters using ADS-B data.

Student: T.W. Gloudemans
2016 ADS-B data and signal quality analysis for surveillance purposes

This research use MLAT data to assist the analysis of ADS-B data and signal quality and to investigate the effects of internal and external factors on ADS-B performance implementation.

Student: T.L. Verbraak

Other research projects

Active Engage Knowledge Transfer Network
Engage is managed by a consortium of academia and industry, with the support of the SESAR JU, to promote and facilitate the development of air traffic management research in Europe. The project forces on inspiring new researchers and helping to align exploratory and industrial research, through a wide range of activities and financial support actions.

© Copyright 2020 Junzi Sun