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Grondradar Data Analysis for Risk Identification
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Grondradar Data Analysis for Risk Identification
Functie
The internship assignment
At Schiphol every year approximately 500.000 (air) movements take place (pre-corona). Simultaneously, other vehicles than aircraft, such as: cars and pushback trucks make use of the roads and taxiways on the airports surface. With help of the ground radar, all these vehicles can be monitored and directed by the air traffic and apron controllers which job it is to maintain an orderly, efficient and safe flow of traffic. However, it sometimes still occurs that certain vehicles lose their separation which can have serious consequences such as ground collisions, taxiway/runway excursions, runway incursions or other damage to aircraft/vehicles or the airport infrastructure.The occurrences described above are usually well reported. However, less serious occurrences such as a small loss of separation or preventive measures to prevent incidents (emergency braking/evasive maneuver) are less prevalent and therefore hard to monitor, especially since these occurrences are not always reported. To still be able to identify these occurrences, in a previous study, NLR has developed algorithms that identify certain events from the ground radar. Since ground radar is always stored, identification of certain events should not anymore be completely reliant on whether the occurrence has been reported or not. The results of the analysis can be used in risk analysis tools to help estimate the probability that some event occurs.
This assignment focusses on further development of these algorithms. The previously developed algorithms should be evaluated and new algorithms for identification of different events should be developed. The assignment includes a literature study into the various methods which can be used for identifying outliers in the ground radar dataset.
Result
- Literature study of state of the art methods for identifying outliers in ground radar data
- Python code for the following workpackages:
- Data Preprocessing
- Algorithms for detection of certain events
- Visualisation of the results.
- Report of the approach, algorithms, results and evaluation of the work done.
Duration of the assignment
To be agreed, 5 to 6 months. Expected start date: September 2021.Profiel
We are looking for:
- HBO/WO Level;
- Demonstrable knowledge and interest in programming languages (Python, R);
- Demonstrable knowledge and interest in data science tools/frameworks;
- Interest in aviation;
- Independent working attitude;
- Fluent in English; both oral and written;
- Experience with version control such as Git is a pré.
Arbeidsvoorwaarden
What we offer
- A challenging internship in a high-tech working place;
- Informal company culture with room for initiative, valuing a result-oriented and committed attitude;
- An internship compensation.
Informatie
Your internship company
Royal NLR has been an ambitious knowledge organisation for more than 100 years. We aim at being innovative and make air transport safer and more sustainable, efficient and effective. We are at the base of breakthrough technologies. Plans and ideas may become reality when given the right appreciation. More than 600 enthusiastic professionals are working on research and innovation. Specializations range from aerospace engineers to psychologists and from mathematicians to application engineers. Our site in Amsterdam has been renovated recently, while a brand-new building is realised in Marknesse.Your desk is waiting for you at the AOSI (Aerospace Operations Safety Institute) department of the NLR. AOSI works on different projects for both military and civil customers such as: regulators, civil aviation authorities, airports, airlines and ANSP’s.
Solliciteren
Interested?
Apply by emailing your motivation letter and CV to Vincent de Vries,or contact Vincent de Vries, 088-511 3032.
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