A Machine Learning Framework for Performance Prediction of an Air Surveillance System

Juha Jylhä, Marja Ruotsalainen, Ville Väisänen, Kai Virtanen, Mikko Harju, Minna Väilä

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    1 Citation (Scopus)
    14 Downloads (Pure)

    Abstract

    The optimal use of a surveillance radar system requires proper understanding about the system behavior in different configurations, modes, and operating conditions. This paper proposes a machine learning framework for producing and validating the performance model of the surveillance radar system. The framework consists of an optimization method for the parameterization of a radar model and a machine learning method for the modeling of a tracker. Optimization and machine learning is based on the satellite navigation data of cooperative aircraft and corresponding track data from the surveillance system. The aim is to learn the system performance in a wide range of operating conditions using the extensive measurement history and then to predict the present performance with high accuracy at specified locations in the airspace. The feasibility of the proposed framework is assessed using real data.
    Original languageEnglish
    Title of host publicationThe 14th European Radar Conference (EuRAD 2017)
    PublisherIEEE
    ISBN (Electronic)978-2-87487-049-1
    DOIs
    Publication statusPublished - 11 Oct 2017
    Publication typeA4 Article in a conference publication
    EventEuropean Radar Conference -
    Duration: 1 Jan 1900 → …

    Conference

    ConferenceEuropean Radar Conference
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1

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