Tracking a single pigeon using a shadowing filter algorithm

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high- quality and high-resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase-space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeon's dynamics are Newtonian. We explore not only how to choose the filter's parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filter's approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.

    Original languageEnglish
    Pages (from-to)4419-4431
    Number of pages13
    JournalEcology and Evolution
    Volume7
    Issue number12
    DOIs
    Publication statusPublished - Jun 2017

    Cite this

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    title = "Tracking a single pigeon using a shadowing filter algorithm",
    abstract = "Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high- quality and high-resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase-space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeon's dynamics are Newtonian. We explore not only how to choose the filter's parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filter's approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.",
    keywords = "animal behavior, animal movement, filtering, GPS, shadowing filter, tracking, GPS TRACKING, DYNAMICS",
    author = "Ayham Zaitouny and Thomas Stemler and Michael Small",
    year = "2017",
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    doi = "10.1002/ece3.2976",
    language = "English",
    volume = "7",
    pages = "4419--4431",
    journal = "Ecology and Evolution",
    issn = "2045-7758",
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    Tracking a single pigeon using a shadowing filter algorithm. / Zaitouny, Ayham; Stemler, Thomas; Small, Michael.

    In: Ecology and Evolution, Vol. 7, No. 12, 06.2017, p. 4419-4431.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Tracking a single pigeon using a shadowing filter algorithm

    AU - Zaitouny, Ayham

    AU - Stemler, Thomas

    AU - Small, Michael

    PY - 2017/6

    Y1 - 2017/6

    N2 - Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high- quality and high-resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase-space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeon's dynamics are Newtonian. We explore not only how to choose the filter's parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filter's approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.

    AB - Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high- quality and high-resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase-space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeon's dynamics are Newtonian. We explore not only how to choose the filter's parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filter's approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.

    KW - animal behavior

    KW - animal movement

    KW - filtering

    KW - GPS

    KW - shadowing filter

    KW - tracking

    KW - GPS TRACKING

    KW - DYNAMICS

    U2 - 10.1002/ece3.2976

    DO - 10.1002/ece3.2976

    M3 - Article

    VL - 7

    SP - 4419

    EP - 4431

    JO - Ecology and Evolution

    JF - Ecology and Evolution

    SN - 2045-7758

    IS - 12

    ER -