A RCS model of complex targets for radar performance prediction

Minna Väilä, Juha Jylhä, Ville Väisänen, Henna Perälä, Ari Visa, Mikko Harju, Kai Virtanen

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

    6 Citations (Scopus)
    469 Downloads (Pure)

    Abstract

    The objective of the radar performance prediction is to compute quantities of interest concerning the ability of the radar to observe its surroundings. Besides the properties of the radar system, the performance is affected by the target, whose radar cross section (RCS) is one of the predominant factors. The performance prediction is usually performed in relation to the target RCS characterized by a constant value or a particular statistical distribution. Such representations generalize real-life complex targets rendering them unsuitable for some objectives since the RCS is significantly influenced by the target aspect angle and is inherently stochastic by nature. Thus, a more dynamic description may be valuable e.g. for analyzing the radar performance on a flight path of interest. We propose representing the RCS with a histogram that includes such dynamic properties and is suitable for considering the target in different ways for performance prediction: in a more general manner or dependent on its aspect angle. We consider the case of traditional RCS with low spatial resolution and demonstrate the proposed approach through the probability of detection computed for a generic surveillance radar.
    Original languageEnglish
    Title of host publication2017 IEEE Radar Conference (RadarConf)
    Place of PublicationSeattle, WA, USA
    PublisherIEEE
    Pages430-435
    Number of pages6
    ISBN (Electronic)978-1-4673-8823-8
    ISBN (Print)978-1-4673-8824-5
    DOIs
    Publication statusPublished - 8 May 2017
    Publication typeA4 Article in a conference publication
    EventIEEE Radar Conference -
    Duration: 1 Jan 1900 → …

    Publication series

    Name
    PublisherIEEE
    ISSN (Electronic)2375-5318

    Conference

    ConferenceIEEE Radar Conference
    Period1/01/00 → …

    Keywords

    • Radar cross-sections
    • Histograms
    • Predictive models
    • Aircraft
    • Computational modeling
    • Probability

    Publication forum classification

    • Publication forum level 1

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