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Dr. Junzi Sun
TU Delft

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Research Keywords & Expertise

0 Air Traffic Management
0 Aircraft Performance
0 Trajectory Optimization
0 Data Science
0 Aircraft Surveillance

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Aircraft Performance
Aircraft Surveillance
Air Traffic Management

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Journal article
Published: 23 July 2020 in Aerospace
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Air traffic simulations serve as common practice to evaluate different concepts and methods for air transportation studies. The aircraft performance model is a key element that supports these simulation-based studies. It is also an important component for simulation-independent studies, such as air traffic optimization and prediction studies. Commonly, contemporary studies have to rely on proprietary aircraft performance models that restrict the redistribution of the data and code. To promote openness and research comparability, an alternative open performance model would be beneficial for the air transportation research community. In this paper, we introduce an open aircraft performance model (OpenAP). It is an open-source model that is based on a number of our previous studies, which were focused on different components of the aircraft performance. The unique characteristic of OpenAP is that it was built upon open aircraft surveillance data and open literature models. The model is composed of four main components, including aircraft and engine properties, kinematic performances, dynamic performances, and utility libraries. Alongside the performance model, we are publishing an open-source toolkit to facilitate the use of this model. The main objective of this paper is to describe each main component, their connections, and how they can be used for simulation and research in practice. Finally, we analyzed the performance of OpenAP by comparing it with an existing performance model and sample flight data.

ACS Style

Junzi Sun; Jacco M. Hoekstra; Joost Ellerbroek. OpenAP: An Open-Source Aircraft Performance Model for Air Transportation Studies and Simulations. Aerospace 2020, 7, 104 .

AMA Style

Junzi Sun, Jacco M. Hoekstra, Joost Ellerbroek. OpenAP: An Open-Source Aircraft Performance Model for Air Transportation Studies and Simulations. Aerospace. 2020; 7 (8):104.

Chicago/Turabian Style

Junzi Sun; Jacco M. Hoekstra; Joost Ellerbroek. 2020. "OpenAP: An Open-Source Aircraft Performance Model for Air Transportation Studies and Simulations." Aerospace 7, no. 8: 104.

Journal article
Published: 26 February 2020 in Transportation Research Part C: Emerging Technologies
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In air traffic management research, aircraft performance models are often used to generate and analyze aircraft trajectories. Although a crucial part of the aircraft performance model, the aerodynamic property of aircraft is rarely available for public research purposes, as it is protected by aircraft manufacturers for commercial reasons. In many studies, a simplified quadratic drag polar model is assumed to compute the drag of an aircraft based on the required lift. In this paper, using surveillance data, we take on the challenge of estimating the drag polar coefficients based on a novel stochastic total energy model that employs Bayesian computing. The method is based on a stochastic hierarchical modeling approach, which is made possible given accurate open aircraft surveillance data and additional analytical models from the literature. Using this proposed method, the drag polar models for 20 of the most common aircraft types are estimated and summarized. By combining additional data from the literature, we propose additional methods allowing aircraft total drag to be calculated under other configurations, such as when flaps and landing gears are deployed. We also include additional models allowing the calculation of wave drag caused by compressibility at high Mach number. Though uncertainties exist, it has been found that the estimated drag polars agree with existing models, as well as CFD simulation results. The trajectory data, performance models, and results related to this study are shared publicly.

ACS Style

Junzi Sun; Jacco M. Hoekstra; Joost Ellerbroek. Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model. Transportation Research Part C: Emerging Technologies 2020, 114, 391 -404.

AMA Style

Junzi Sun, Jacco M. Hoekstra, Joost Ellerbroek. Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model. Transportation Research Part C: Emerging Technologies. 2020; 114 ():391-404.

Chicago/Turabian Style

Junzi Sun; Jacco M. Hoekstra; Joost Ellerbroek. 2020. "Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model." Transportation Research Part C: Emerging Technologies 114, no. : 391-404.

Proceedings
Published: 01 January 2020 in Proceedings
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Mode S surveillance allows air traffic controllers to interrogate certain information from aircraft, such as airspeeds, turn parameters, target altitudes, and meteorological conditions. However, not all aircraft have enabled the same capabilities. Before performing any specific interrogation, the surveillance radar must acquire the transponder capabilities of an aircraft. This is obtained via the common usage Ground-initiated Comm-B (GICB) capabilities report (BDS 1,7). With this report, third-party researchers can further improve the identification accuracy of different Mode S Comm-B message types, as well as study the compliance of surveillance standards. Thanks to the OpenSky network’s large-scale global coverage, a full picture of current Mode S capabilities over the world can be constructed. In this paper, using the OpenSky Impala data interface, we first sample over one month of raw BDS 1,7 messages from around the world. Around 40 million messages are obtained. We then decode and analyze the GICB capability messages. The resulting data contain Comm-B capabilities for all aircraft available to OpenSky during this month. The analyses in this paper focus on exploring statistics of GICB capabilities among all aircraft and within each aircraft type. The resulting GICB capability database is shared as an open dataset.

ACS Style

Junzi Sun; Huy Vû; Xavier Olive; Jacco M. Hoekstra. Mode S Transponder Comm-B Capabilities in Current Operational Aircraft. Proceedings 2020, 59, 4 .

AMA Style

Junzi Sun, Huy Vû, Xavier Olive, Jacco M. Hoekstra. Mode S Transponder Comm-B Capabilities in Current Operational Aircraft. Proceedings. 2020; 59 (1):4.

Chicago/Turabian Style

Junzi Sun; Huy Vû; Xavier Olive; Jacco M. Hoekstra. 2020. "Mode S Transponder Comm-B Capabilities in Current Operational Aircraft." Proceedings 59, no. 1: 4.

Proceedings
Published: 01 January 2020 in Proceedings
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The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation.

ACS Style

Xavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings 2020, 59, 8 .

AMA Style

Xavier Olive, Junzi Sun, Adrien Lafage, Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings. 2020; 59 (1):8.

Chicago/Turabian Style

Xavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. 2020. "Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches." Proceedings 59, no. 1: 8.

Conference paper
Published: 23 December 2019 in EPiC Series in Computing
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A large quantity of Mode S data is being gathered by the OpenSky receiver network every day. Information regarding common flight states, such as position, ground speed, and the vertical rate is broadcast by ADS-B and has already been decoded and made available for researchers via the OpenSky historical database. However, there is still a large amount of Mode S communication data that has not yet been fully explored. Specifically, the information contained in Enhanced Mode S Surveillance downlink messages can be utilized to better support ATM research. The challenge of decoding such information lies in the implicit inference process for Mode S Comm-B messages. This paper presents a new open library, pymodes-opensky, which connects the existing open-source pyModeS decoder to the raw Mode S messages from the OpenSky historical database through the Impala shell. It also presents a convenient workflow that can be used to obtain additional information regarding airspeeds, flight intentions, and meteorological conditions of a given flight from the OpenSky database. An analysis based on a global dataset from OpenSky is conducted, and the associated Mode S interrogation statistics in different regions are shown.

ACS Style

Junzi Sun; Jacco Hoekstra. Integrating pyModeS and OpenSky Historical Database. EPiC Series in Computing 2019, 67, 63 -72.

AMA Style

Junzi Sun, Jacco Hoekstra. Integrating pyModeS and OpenSky Historical Database. EPiC Series in Computing. 2019; 67 ():63-72.

Chicago/Turabian Style

Junzi Sun; Jacco Hoekstra. 2019. "Integrating pyModeS and OpenSky Historical Database." EPiC Series in Computing 67, no. : 63-72.

Journal article
Published: 06 June 2019 in Transportation Research Part C: Emerging Technologies
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This article investigates the estimation of aircraft mass and thrust settings of departing aircraft 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 only aircraft surveillance data, flight states such as position, velocity, wind speed, and air temperature are collected and used for the estimations. With the regularized Sample Importance Re-sampling particle filter, we are able to estimate the aircraft mass within 30 seconds once an aircraft is airborne. Using this short flight segment allows the assumption of constant mass and thrust setting. The segment at the start of the climb also represents the time when maximum thrust setting is most likely to occur. This study emphasizes an important aspect of the estimation problem, the observation noise modeling. Four observation noise models are proposed, which are all based on the native navigation accuracy parameters that have been obtained automatically from the surveillance data. Simulations and experiments are conducted to test the theoretical model. The results show that the particle filter is able to quantify uncertainties, as well as determine the noise limit for an accurate estimation. The method of this study is tested with a data-set consisting of 50 Cessna Citation II flights where true masses were recorded.

ACS Style

Junzi Sun; Henk Blom; Joost Ellerbroek; Jacco M. Hoekstra. Particle filter for aircraft mass estimation and uncertainty modeling. Transportation Research Part C: Emerging Technologies 2019, 105, 145 -162.

AMA Style

Junzi Sun, Henk Blom, Joost Ellerbroek, Jacco M. Hoekstra. Particle filter for aircraft mass estimation and uncertainty modeling. Transportation Research Part C: Emerging Technologies. 2019; 105 ():145-162.

Chicago/Turabian Style

Junzi Sun; Henk Blom; Joost Ellerbroek; Jacco M. Hoekstra. 2019. "Particle filter for aircraft mass estimation and uncertainty modeling." Transportation Research Part C: Emerging Technologies 105, no. : 145-162.

Journal article
Published: 20 May 2019 in IEEE Transactions on Intelligent Transportation Systems
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The availability of low-cost automatic dependent surveillance-broadcast (ADS-B) receivers has given researchers the ability to make use of large amounts of aircraft state data. This data is being used to support air transportation research in performance study, trajectory prediction, procedure analysis, and airspace design. However, aircraft states contained in ADS-B messages are limited. More performance parameters are downlinked as Mode-S Comm-B replies, upon the automatic and periodic interrogation of air traffic control secondary surveillance radar. These replies reveal aircraft airspeed, turn rate, target altitude, and so on. They can be intercepted using the same 1090-MHz receiver that receives the ADS-B messages. However, a third-party observer does not know the interrogations, which originated the Comm-B replies. Thus, it is difficult to decode these messages without knowing the type and source aircraft. Furthermore, the parity check also cannot be performed without knowing the interrogations. In this paper, we propose a new heuristic-probabilistic method to decode the Comm-B replies and to check the correctness of the messages. Based on a reference dataset provided by air traffic control of the Netherlands, the method yields a success rate of 97.68% with an error below 0.01%. The performance of the proposed method is further examined with data from eight different regions of the world. The implementation of the inference and decoding process, pyModeS, is shared as an open-source library.

ACS Style

Junzi Sun; Huy Vu; Joost Ellerbroek; Jacco M. Hoekstra. pyModeS: Decoding Mode-S Surveillance Data for Open Air Transportation Research. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 2777 -2786.

AMA Style

Junzi Sun, Huy Vu, Joost Ellerbroek, Jacco M. Hoekstra. pyModeS: Decoding Mode-S Surveillance Data for Open Air Transportation Research. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (7):2777-2786.

Chicago/Turabian Style

Junzi Sun; Huy Vu; Joost Ellerbroek; Jacco M. Hoekstra. 2019. "pyModeS: Decoding Mode-S Surveillance Data for Open Air Transportation Research." IEEE Transactions on Intelligent Transportation Systems 21, no. 7: 2777-2786.

Journal article
Published: 30 November 2018 in Transportation Research Part C: Emerging Technologies
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Open access to flight data from Automatic Dependent Surveillance-Broadcast (ADS-B) has provided researchers with 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. In this paper, a set of more than thirty parameters from seven distinct flight phases are extracted for common commercial aircraft types. It uses various data mining methods, as well as a maximum likelihood estimation approach to generate parametric models for these performance parameters. All parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing. Both analytical results and summaries are shown. When available, optimal parameters from these models are also compared with the Base of Aircraft Data and the Eurocontrol aircraft performance database. This research presents a comprehensive set of methods for extracting different aircraft performance parameters. It also provides the first set of open parametric performance data for common aircraft types. All model data are published as open data under a flexible open-source license.

ACS Style

Junzi Sun; Joost Ellerbroek; Jacco M. Hoekstra. WRAP: An open-source kinematic aircraft performance model. Transportation Research Part C: Emerging Technologies 2018, 98, 118 -138.

AMA Style

Junzi Sun, Joost Ellerbroek, Jacco M. Hoekstra. WRAP: An open-source kinematic aircraft performance model. Transportation Research Part C: Emerging Technologies. 2018; 98 ():118-138.

Chicago/Turabian Style

Junzi Sun; Joost Ellerbroek; Jacco M. Hoekstra. 2018. "WRAP: An open-source kinematic aircraft performance model." Transportation Research Part C: Emerging Technologies 98, no. : 118-138.

Research article
Published: 03 October 2018 in PLOS ONE
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Wind and temperature data are important parameters in aircraft performance studies. The lack of accurate measurements of these parameters forces researchers to rely on numerical weather prediction models, which are often filtered for a larger area with decreased local accuracy. Aircraft, however, also transmit information related to weather conditions, in response to interrogation by air traffic controller surveillance radars. Although not intended for this purpose, aircraft surveillance data contains information that can be used for weather models. This paper presents a method that can be used to reconstruct a weather field from surveillance data that can be received with a simple 1090 MHz receiver. Throughout the paper, we answer two main research questions: how to accurately infer wind and temperature from aircraft surveillance data, and how to reconstruct a real-time weather grid efficiently. We consider aircraft as moving sensors that measure wind and temperature conditions indirectly at different locations and flight levels. To address the first question, aircraft barometric altitude, ground velocity, and airspeed are decoded from down-linked surveillance data. Then, temperature and wind observations are computed based on aeronautical speed conversion equations. To address the second question, we propose a novel Meteo-Particle (MP) model for constructing the wind and temperature fields. Short-term local prediction is also possible by employing a predictor layer. Using an unseen observation test dataset, we are able to validate that the mean absolute errors of inferred wind and temperature using MP model are 67% and 26% less than using the interpolated model based on GFS reanalysis data.

ACS Style

Junzi Sun; Huy Vû; Joost Ellerbroek; Jacco M. Hoekstra. Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model. PLOS ONE 2018, 13, e0205029 .

AMA Style

Junzi Sun, Huy Vû, Joost Ellerbroek, Jacco M. Hoekstra. Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model. PLOS ONE. 2018; 13 (10):e0205029.

Chicago/Turabian Style

Junzi Sun; Huy Vû; Joost Ellerbroek; Jacco M. Hoekstra. 2018. "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model." PLOS ONE 13, no. 10: e0205029.

Journal article
Published: 01 May 2018 in Transportation Research Part C: Emerging Technologies
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Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass.

ACS Style

Junzi Sun; Joost Ellerbroek; Jacco M. Hoekstra. Aircraft initial mass estimation using Bayesian inference method. Transportation Research Part C: Emerging Technologies 2018, 90, 59 -73.

AMA Style

Junzi Sun, Joost Ellerbroek, Jacco M. Hoekstra. Aircraft initial mass estimation using Bayesian inference method. Transportation Research Part C: Emerging Technologies. 2018; 90 ():59-73.

Chicago/Turabian Style

Junzi Sun; Joost Ellerbroek; Jacco M. Hoekstra. 2018. "Aircraft initial mass estimation using Bayesian inference method." Transportation Research Part C: Emerging Technologies 90, no. : 59-73.

Brief report
Published: 01 October 2017 in Journal of Aerospace Information Systems
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AUTOMATIC dependent surveillance–broadcast (ADS-B) [1,2] is widely implemented in modern commercial aircraft and will become mandatory equipment in 2020. Flight state information such as position, velocity, and vertical rate are broadcast by tens of thousands of aircraft around the world constantly using onboard ADS-B transponders. These data are identified by a 24-bit International Civil Aviation Organization (ICAO) address, are unencrypted, and can be received and decoded with simple ground station set-ups. This large amount of open data brings a huge potential for ATM research. Most studies that rely on aircraft flight data (historical or real-time) require knowledge on the flight phase of each aircraft at a given time [3–7]. However, when dealing with large datasets such as from ADS-B, which can contain many tens of thousands of flights, exceptions to deterministic definitions of flight phases are inevitable, due to large variances in climb rate, altitude, velocity, or a combination of these. In this case, instead of using deterministic logic to process and extract flight data based on flight conventions, robust and versatile identification algorithms are required. In this paper, a twofold method is proposed and tested: 1) a machine learning clustering step that can handle large amounts of scattered ADS-B data to extract continuous flights, and 2) a flight phase identification step that can segment flight data of any type of aircraft and trajectory by different flight phases

ACS Style

Junzi Sun; Joost Ellerbroek; Jacco Hoekstra. Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets. Journal of Aerospace Information Systems 2017, 14, 566 -572.

AMA Style

Junzi Sun, Joost Ellerbroek, Jacco Hoekstra. Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets. Journal of Aerospace Information Systems. 2017; 14 (10):566-572.

Chicago/Turabian Style

Junzi Sun; Joost Ellerbroek; Jacco Hoekstra. 2017. "Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets." Journal of Aerospace Information Systems 14, no. 10: 566-572.