Many modern day collaborative systems/system of systems rely heavily on the sharing of information in order to improve performance, manage resources, and maximize overall capability. These types of systems are characterized by many decentralized nodes that can either all be identical or partitioned into a finite set of specialized types. When information is shared within any system of systems, the overall performance hinges on its ability to correctly associate the information received. The primary hypothesis evaluated by any system of systems after the receipt of new information is to determine whether this information belongs to a previously observed entity, or not. When this hypothesis is false, the new information belongs to a new entity, which includes both real and false entities. To evaluate this hypothesis, and to determine the optimal assignments to make at each time step, a data association discriminator or scoring function that performs like a distance function between two probability distributions with common support is defined. This paper defines the properties desired of a data association discriminator, highlights the measures of information that satisfy these properties, and develops the corresponding gating and scoring equations for use during the data association process. The most commonly employed gating and scoring function in the data association literature is the square of the Mahalanobis distance and the log-likelihood score function, which are only defined between two multivariate Gaussian distributions. One of the objectives of this paper is to demonstrate the superior characteristics of the data association discriminators presented herein when used in determining the optimal assignments as compared with the log-likelihood score function. Toward this end, and due to the prevalence of the multivariate Gaussian distribution function in the general entity tracking and information fusion literature, the closed-form equations for the data association discriminators based on the statistical f divergences, along with the other measures of information, namely, entropy and mutual information, for multivariate Gaussian distributions will be presented. The architecture upon which a system of systems is developed and designed plays a fundamental role when selecting an appropriate association discriminator. This paper discusses various measures of information commonly used between and within the components of these types of systems and compares and contrasts their behavior in a common framework with a focus on the data association problem. Commonly used measures of information, namely, differential entropy, mutual information, and the statistical divergences, along with the log-likelihood score function are all examined analytically in a common mathematical framework, the advantages and disadvantages of each are discussed, new results are derived and presented, and several numerical examples based on synthetic data are presented that illuminate their behaviors and characteristics. Lastly, it is demonstrated that the Kullback–Leibler discriminator/divergence is not the best choice for use as a data association discriminator.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Associating Data in System of Systems Using Measures of Information


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2014-04-09


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Associating Data in System of Systems Using Measures of Information

    Clancy, D. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2011


    SYSTEM AND METHOD FOR ASSOCIATING TIRE INFORMATION WITH TOWABLE RIG INFORMATION

    GEROWITZ MICHAEL L / HOSTETLER JOSHUA A / STEVENS JEFFREY L | Europäisches Patentamt | 2020

    Freier Zugriff

    ASSOCIATING IMAGE DATA AND DEPTH DATA FOR DEPTH DETERMINATION

    DOWDALL JONATHAN TYLER / GOEL KRATARTH / POLLACK ADAM EDWARD et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    SYSTEM AND METHOD FOR DETECTING AND ASSOCIATING RAILWAY RELATED DATA

    LATA VLAD / BRANDLHUBER CHRISTIAN / BÖHM THOMAS | Europäisches Patentamt | 2019

    Freier Zugriff