Project handbook
The NEEDED project handbook outlines management guidelines, roles, and procedures to ensure effective project execution. It details strategies for coordination, quality control, risk management, and communication, aiming to streamline the project’s activities and objectives.
Data management plan
NEEDED’s Data Management Plan is a comprehensive document that outlines the procedures for data collection, storage, sharing, and protection. It ensures that project data is managed efficiently, remains accessible to stakeholders, and complies with legal and ethical standards.
Dissemination and communication plan
NEEDED’s Dissemination and Communication Plan outlines strategies for effectively sharing project results, engaging with stakeholders, and increasing public awareness. It aims to ensure clear communication and promote the project’s impact and achievements.
Dissemination and communication plan – RP1 update
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Abstract—The analysis of aircraft surface trajectories based on
open-access Automatic Dependent Surveillance–Broadcast (ADS-B)
data is affected by the low quality and resolution of received data.
Indeed, the quality of ground ADS-B data is subject to the coverage
of the crowdsourced ADS-B receivers. Moreover, the quality of
GNSS signals received and processed by aircraft while on ground,
which is subject to various sources of interference and reflections
of signals, affects the ADS-B data quality. In this study, we present
a model to filter ground data and interpolate trajectories along
runways, taxiways, aprons, and parking positions, which are also
available as public information in OpenStreetMap. The proposed
model functions as a Kalman smoother, which takes the distance
of the trajectory to the known structure of the taxiway system as
a constraint. We validate the efficacy of this model using various
trajectories featuring partially missing ADS-B data. Furthermore,
we discuss the limitations of the model when confronted with
extensive data gaps and open the discussion regarding potential
use cases enabled by improved processing of ground trajectory
data.
Link: https://drive.google.com/file/d/1mybTugexAsCwKeyt52G_U37swaER_dmC/view
Since 2020, all commercial aircraft have been mandated to be equipped with ADS-B Out
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beamforming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADSB Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated.
Link: https://drive.google.com/file/d/1mybTugexAsCwKeyt52G_U37swaER_dmC/view
To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where
L_{den} = 45 – 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague
airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented.
Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.
To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where
L_{den} = 45 – 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague
airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented.
Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.
