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Huy Vû
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1
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.
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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.
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.
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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.
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. ...
using MP model are 67% and 26% less than using the interpolated model based
on GFS reanalysis data. ...
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.
using MP model are 67% and 26% less than using the interpolated model based
on GFS reanalysis data.
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.
...
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.