Tracking rigid bodies using only position data: A shadowing filter approach based on newtonian dynamics

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    3 Citations (Scopus)

    Abstract

    Tracking a moving object like a ship, vehicle, aircraft or even an animal or human is challenging especially when given only noisy observations of the path. To successfully track such an object the method used needs to infer the unknown information, for example the acceleration, from noisy position data. Here we present such a method based on a shadowing filter algorithm. In comparison with other filters such as Kalman and Particle filters the shadowing filter solves the tracking problem based on deterministic dynamics instead of statistics. The algorithm presented shows how to track rigid bodies having an unknown moment of inertia and we validate the performance of the filter and explore how the two important parameters of the filter impact on its performance.

    Original languageEnglish
    Pages (from-to)81-90
    Number of pages10
    JournalDigital Signal Processing
    Volume67
    DOIs
    Publication statusPublished - 1 Aug 2017

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    title = "Tracking rigid bodies using only position data: A shadowing filter approach based on newtonian dynamics",
    abstract = "Tracking a moving object like a ship, vehicle, aircraft or even an animal or human is challenging especially when given only noisy observations of the path. To successfully track such an object the method used needs to infer the unknown information, for example the acceleration, from noisy position data. Here we present such a method based on a shadowing filter algorithm. In comparison with other filters such as Kalman and Particle filters the shadowing filter solves the tracking problem based on deterministic dynamics instead of statistics. The algorithm presented shows how to track rigid bodies having an unknown moment of inertia and we validate the performance of the filter and explore how the two important parameters of the filter impact on its performance.",
    keywords = "Optimization, Shadowing filter, State estimation, Tracking",
    author = "Zaitouny, {Ayham A.} and Thomas Stemler and Kevin Judd",
    year = "2017",
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    doi = "10.1016/j.dsp.2017.04.004",
    language = "English",
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    T1 - Tracking rigid bodies using only position data

    T2 - A shadowing filter approach based on newtonian dynamics

    AU - Zaitouny, Ayham A.

    AU - Stemler, Thomas

    AU - Judd, Kevin

    PY - 2017/8/1

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    N2 - Tracking a moving object like a ship, vehicle, aircraft or even an animal or human is challenging especially when given only noisy observations of the path. To successfully track such an object the method used needs to infer the unknown information, for example the acceleration, from noisy position data. Here we present such a method based on a shadowing filter algorithm. In comparison with other filters such as Kalman and Particle filters the shadowing filter solves the tracking problem based on deterministic dynamics instead of statistics. The algorithm presented shows how to track rigid bodies having an unknown moment of inertia and we validate the performance of the filter and explore how the two important parameters of the filter impact on its performance.

    AB - Tracking a moving object like a ship, vehicle, aircraft or even an animal or human is challenging especially when given only noisy observations of the path. To successfully track such an object the method used needs to infer the unknown information, for example the acceleration, from noisy position data. Here we present such a method based on a shadowing filter algorithm. In comparison with other filters such as Kalman and Particle filters the shadowing filter solves the tracking problem based on deterministic dynamics instead of statistics. The algorithm presented shows how to track rigid bodies having an unknown moment of inertia and we validate the performance of the filter and explore how the two important parameters of the filter impact on its performance.

    KW - Optimization

    KW - Shadowing filter

    KW - State estimation

    KW - Tracking

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    JF - Digital Signal Processing

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