using MP model are 67% and 26% less than using the interpolated model based

on GFS reanalysis data.","","en","journal article","","","","","","","","","","","","","","" "uuid:611693b2-046a-4cbc-a61b-d1793e52f523","http://resolver.tudelft.nl/uuid:611693b2-046a-4cbc-a61b-d1793e52f523","Aircraft initial mass estimation using Bayesian inference method","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2018","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.","Aircraft mass; Weight estimation; Bayesian inference","en","journal article","","","","","","Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.","","2018-09-10","","","","","","" "uuid:2b02d90b-9c33-49af-82bb-d32fd4061919","http://resolver.tudelft.nl/uuid:2b02d90b-9c33-49af-82bb-d32fd4061919","Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method","Sun, J. (TU Delft Control & Simulation); Blom, H.A.P. (TU Delft Aerospace Transport & Operations; NLR - Netherlands Aerospace Centre); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2018","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 used for the estimation. An important aspect of particle filtering is noise modeling. Four noise models are proposed in this paper based on the native ADS-B Navigation Accuracy Category (NAC) parameters. Simulations, experiments, and validation, based on a number of flights are carried out to test the theory. As a result, convergence of the estimation can usually be obtained within 30 seconds for any climbing flight. The method proposed in this paper not only provides final estimates, but also defines the limits of noise above which estimation of mass and thrust becomes impossible. When validated with a dataset consisting of the measured true mass and thrust of 50 Cessna Citation II flights, the stochastic recursive Bayesian approach proposed in this paper yields a mean absolute error of 4.6%.","aircraft; state estimation; point-mass model; measurement noise; particle filter; Bayesian estimation","en","conference paper","","","","","","","","","","","","","","" "uuid:9977a616-4506-4acb-9d27-b9e27f9ddb8d","http://resolver.tudelft.nl/uuid:9977a616-4506-4acb-9d27-b9e27f9ddb8d","Aircraft Drag Polar Estimation Based on a Stochastic Hierarchical Model","Sun, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation)","","2018","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 models are often not necessary in air traffic management related research, as they often use a simplified point-mass aircraft performance model. In these studies, a simple quadratic relation often assumed to compute the drag of an aircraft based on the required lift. This so-called drag polar describes an approximation of the drag coefficient based on the total lift coefficient. The two key parameters in the drag polar are the zero-lift drag coefficient and the factor to calculate the lift-induced part of the drag coefficient. Thanks to this simplification of the flight model together with accurate flight data, we are able to estimate these aerodynamic parameters based on flight data. In this paper, we estimate the drag polar based on a novel stochastic total energy model using Bayesian computing and Markov chain Monte Carlo sampling. The method is based on the stochastic hierarchical modeling approach. With sufficiently accurate flight data and some basic knowledge of aircraft and their engines, the drag polar can be estimated. We also analyze the results and compare them to the commonly used Base of Aircraft Data model. The mean absolute difference among 20 common aircraft for zero-lift drag coefficient and lift-induced drag factor are 0.005 and 0.003 respectively. At the end of this paper, the drag polar models in different flight phases for these common commercial aircraft types are shared.","aircraft performance; drag polar; aerodynamic coefficient; Bayesian computing; MCMC","en","conference paper","","","","","","","","2019-07-01","","","","","","" "uuid:07a919b4-6320-4cdf-a979-b55fa4b6864e","http://resolver.tudelft.nl/uuid:07a919b4-6320-4cdf-a979-b55fa4b6864e","Bayesian Inference of Aircraft Initial Mass","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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 specific parts of the flight. Often due to inaccurate input data or biased assumptions, a significant number of estimates can result outside of the weight limitation boundaries. This paper proposes an approach that makes use of multiple observations to get a better estimate for a complete flight. By looking at flight data from a complete trajectory and calculating aircraft mass at different flight phases based on different methods, together with fuel flow models, multiple observations of aircraft initial mass can then be derived. Using the Bayesian inference method, final estimates can be made with a higher level of confidence.","aircraft mass; weight estimation; Bayesian inference","en","conference paper","","","","","","","","","","","","","","" "uuid:aa0f7ba7-5fe7-46b6-880b-fa9004eeaae4","http://resolver.tudelft.nl/uuid:aa0f7ba7-5fe7-46b6-880b-fa9004eeaae4","Modeling aircraft performance parameters with open ADS-B data","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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. 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 Eurocontrol aircraft performance database. This research not only presents a comprehensive set of methods for extracting different aircraft performance parameters but also provides a first part of open-source parametric performance models that is ready to be used by the ATM community.","ADS-B; Aircraft performance; Data mining; Maximum likelihood estimation","en","other","","","","","","","","","","","","","","" "uuid:566304e6-99df-4164-96b3-6f73e6b958d7","http://resolver.tudelft.nl/uuid:566304e6-99df-4164-96b3-6f73e6b958d7","Large-Scale ADS-B Data and Signal Quality Analysis","Verbraak, T.L.; Ellerbroek, J. (TU Delft Control & Simulation); Sun, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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 errors. The conclusions from this analysis show that ADS-B indeed is a promising technology, where aircraft are able to accurately report their navigational parameters, but that external factors (e.g., reception probability and malfunctioning on-board equipment) can cause issues with the usability of ADS-B as a primary means of surveillance.","ADS-B; surveillance; latency; accuracy; update interval; integrity; availability","en","conference paper","","","","","","","","","","","","","","" "uuid:a34654cb-a9eb-4d0a-bb75-93971a1750a1","http://resolver.tudelft.nl/uuid:a34654cb-a9eb-4d0a-bb75-93971a1750a1","Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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.","","en","journal article","","","","","","","","","","","","","","" "uuid:cf180450-cf66-4532-9c0d-ff64159b0901","http://resolver.tudelft.nl/uuid:cf180450-cf66-4532-9c0d-ff64159b0901","Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model","Sun, J. (TU Delft Control & Simulation); Vû, Huy; Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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 measure wind data at different altitudes with the exception of weather balloon systems that cannot be easily scaled. On the other hand, aircraft, which rely heavily on atmospheric data, can be part of atmospheric model itself. Aircraft can provide wind and temperature measurements to ground observers. In this paper, aircraft are considered as a moving sensor network established to re-construct the wind field on a larger scale. Based on the powerful open-source tool pyModeS, aircraft ground velocity and airspeed are decoded from ADS-B and Mode-S data respectively. Wind observations are then derived based on the difference of these two vectors. An innovative gas particle model is also developed so that the complete wind field can be constructed continuously based on these observations. The model can generate wind field in real-time and at all flight levels. Furthermore, the confidence of wind at any 4D position can be computed according to the proposed model method. Multiple selfand cross-validations are conducted to ensure the correctness and stability of the model, as well as the resulting wind field. This paper provides a series of novel methods, as well as open-source tools, that enable the research community using simple ADS-B/Mode-S receivers to construct accurate wind fields.","ADS-B; Mode-S; aviation weather; wind modeling; aircraft sensor network; gas particle model","en","conference paper","","","","","","","","","","","","","","" "uuid:7dc6ecdc-28f4-4d07-a08b-494be45d6fb6","http://resolver.tudelft.nl/uuid:7dc6ecdc-28f4-4d07-a08b-494be45d6fb6","Modeling aircraft performance parameters with open ADS-B data","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","","2017","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. 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 Eurocontrol aircraft performance database. This research not only presents a comprehensive set of methods for extracting different aircraft performance parameters but also provides a first part of open-source parametric performance models that is ready to be used by the ATM community.","ADS-B; Aircraft performance; Data mining; Maximum likelihood estimation","en","conference paper","","","","","","","","","","","","","","" "uuid:234f1650-d9bd-4985-a70e-2115ac9e5c95","http://resolver.tudelft.nl/uuid:234f1650-d9bd-4985-a70e-2115ac9e5c95","Modeling and Inferring Aircraft Takeoff Mass from Runway ADS-B Data","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","Lovell, D. (editor); Fricke, H. (editor)","2016","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 aircraft at its takeoff phase. The first is by studying the kinetic model at lift-off moment. The second is to look at the motion of aircraft on the runway at each sample moment to estimate the mass recursively.","aircraft mass; performance modeling; weight estimation; BlueSky","en","conference paper","","","","","","","","","","","","","","" "uuid:af67a6bd-d812-474d-a304-7a594991390b","http://resolver.tudelft.nl/uuid:af67a6bd-d812-474d-a304-7a594991390b","Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods","Sun, J. (TU Delft Control & Simulation); Ellerbroek, J. (TU Delft Control & Simulation); Hoekstra, J.M. (TU Delft Control & Simulation)","Lovell, D. (editor); Fricke, H. (editor)","2016","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 decoded from ADSB messages daily. These large amounts of traffic data can be of benefit in a broad range of ATM investigations that rely on operational data and statistics. This paper approaches the challenge of identifying and categorizing these large amounts of data, by proposing various machine learning and fuzzy logic methods. The objective of this paper is to derive a set of methods and reusable open source libraries for handling the large quantity of aircraft flight data.","machine learning; ATM data; big data; fuzzy logic; BlueSky","en","conference paper","","","","","","","","","","","","","",""