07/12/2021
Marie Skłodowska-Curie Actions

MSCA-COFUND-CLEAR-Doc - PhD Position #CD21-43 "GNSS noise prediction based on deep networks with the supervision of 3D city models"

This job offer has expired


  • ORGANISATION/COMPANY
    Université Gustave Eiffel
  • RESEARCH FIELD
    Computer scienceModelling tools
    EngineeringAerospace engineering
    EngineeringCommunication engineering
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    21/03/2022 17:00 - Europe/Brussels
  • LOCATION
    France › Lille
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/10/2022
  • EU RESEARCH FRAMEWORK PROGRAMME
    H2020 / Marie Skłodowska-Curie Actions COFUND
  • MARIE CURIE GRANT AGREEMENT NUMBER
    101034248

OFFER DESCRIPTION

With the emergence of autonomous vehicles, performance requirements for localisation solutions are strongly growing. Indeed, the localisation information needs to be provided with accuracy, but also availability, and more and more with integrity, i.e. with a certain guarantee on the level of performance provided by the system, in particular when dealing with safety of the users.

GNSS positioning systems are widely used because of their (cheap) cost, their provision of an absolute position, wherever and whenever the user needs. According to the market report released by EUSPA (EU Agency for the Space Programme) in 2019, the revenue of GNSS downstream market is reaching €150 billion and out of 1.7 billion GNSS shipped units in 2019, more than 40% will be Galileo. In principle, the GNSS receivers rely on the measurement of time of arrival of several satellite signals simultaneously, in order to estimate the pseudo-range, i.e. distance between each satellite and the user’s receiver, for position estimation. In most of the usages today, the user is an urban user, moving in a city where buildings, trees and interferences, called local effects that degrade the measurements, thus the positioning performance. This is one of major the bottlenecks to extend the GNSS application in urban areas.

The city, unfortunately, generates an unexpected noise for GNSS. This noise is composed of multipath, masking effects, or interferences created by other systems in the surroundings. It has multiple causes and dependencies: the close environment of the user, antenna choice and installation, time (as GNSS satellites are moving around the earth), user dynamics…; it can also differ from one receiver to another one due to various solutions implemented in the receiver, usually used as a black box by the end user. The noise error model has a non-zero mean, large variance, can be non-symmetric with heavy tails.

Although noise and in particular multipath noise concentrates huge efforts from the scientific community, the urban noise remains very complex to model. So many parameters are involved that it is difficult to model it considering physics or mathematics only. Looking at the challenges of GNSS multipath and at the potential of Machine Learning algorithms, it becomes straightforward to investigate the benefits of the artificial intelligence in the GNSS domain.

First Machine Learning related publications focus on multipath and NLOS mitigation [Hsu 2017][Suzuki 2017]; some others on context detection [Gao 2020] (is the user in a city, in a rural environment…?), spoofing detection [Semanjski 2020]. When signal processing can be accessed, some proposals apply machine learning to NLOS Multipath Detection [Suzuki 2020] or Jammer Classification [Ferre 2019] but this is not available from COTS receivers.

In this study, we intend to focus on the use of Machine learning to model the noise observed in urban environments. These models are of primary use for a better localization as previous studies shown that accurate noise modelling can enhance GNSS accuracy [Marais 2010], as well as multi-sensor fusion [Zhang 2018][Chen 2020]. They can be used in enhanced integrity monitoring concepts bounding the errors [No 2021].

The use of these technics raises several issues among which the choice of the relevant features that will characterize the noise the better; the availability of data and 3D building model of large reference database for learning processes; the authentication of the labelled multipath data; the scalability of the datasets and models obtained considering that a city on Hong Kong strongly differ from one in France in terms of masking effects…

The candidate will be part of the LEOST lab from the university Gustave Eiffel and will be welcomed inside the Intelligent Positioning and Navigation Laboratory from the Hong Kong Polytechnic University for the mobility stay. Both teams have a strong scientific background on GNSS, experimentation capacities, as well as the experience of collaborative international projects with academics as well as with industry. He/She will benefit from the past experience of both teams on GNSS local effects detection and mitigation studies, as well as their international network to build his/her research work and prepare the future.

References

Chen P.Y., Chen H., Tsai M.H., Kuo H.K., Tsai Y.M., Chiou T.Y., Jau P.H. “Performance of Machine Learning Models in Determining the GNSS Position Usage for a Loosely Coupled GNSS/IMU System,” ION GNSS+ 2020, virtually, September 21-25, 2020.

Ferre, R. M., Fuente, A. D. La, & Lohan, E. S. (2019). Jammer classification in GNSS bands via machine learning algorithms. Sensors (Switzerland), 19(22). https://doi.org/10.3390/s19224841

Gao H, Groves PD. (2020) Improving environment detection by behavior association for context-adaptive navigation. NAVIGATION, 67:43–60. https://doi.org/10.1002/navi.349

Guohao Zhang, Li-Ta Hsu, Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system, Aerospace Science and Technology, Volume 80, 2018, Pages 368-380. https://doi.org/10.1016/j.ast.2018.07.026.

Marais J., Viandier N., Rabaoui A., Duflos E., GNSS multipath bias models for accurate positioning in urban environments, ITST 2010, 9-11 nov. 2010, Kyoto, Japon.

Hsu L.T. “GNSS Multipath Detection Using a Machine Learning Approach,” IEEE ITSC 2017, Yokohama, Japan.

No H., Milner C., Machine Learning Based Overbound Modeling of Multipath Error for Safety Critical Urban Environment, ION GNSS+ 2021, virtual, September 2021.

Suzuki T., Nakano, Y., Amano, Y. ""NLOS Multipath Detection by Using Machine Learning in Urban Environments,"" ION GNSS+ 2017, Portland, Oregon, pp. 3958-3967.

More Information

Benefits

  • High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
  • International mobility will be mandatory
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.

Eligibility criteria

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree. At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
  • At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
  • At the time of the deadline, applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to the call deadline.
  • Applicants must be available to start the programme on schedule (around 1st October 2022).

Selection process

Additional comments

  • The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
  • Please contact the PhD supervisor for any additional detail on job offer.
  • There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
  • Support service is available during every step of the application process by email: clear-doc@univ-eiffel.fr

Web site for additional job details

Offer Requirements

  • REQUIRED LANGUAGES
    ENGLISH: Good
    FRENCH: Basic

Skills/Qualifications

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.

Specific Requirements

International mobility : A 6-month secondment at Hong-Kong Polytechnic and a 6-month secondment at DLR (Germany). For more information, contact the PhD thesis supervisor.

 

Work location(s)
1 position(s) available at
Université Gustave Eiffel
France
Hauts-de-France
Lille
59666
20 rue Elisée Reclus - BP70317

EURAXESS offer ID: 716472

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