Many sensors in chemical, biological, radiological, and nuclear (CBRN) applications only provide very coarse, integer outputs. For example, chemical detectors based on ion mobility sensing typically have a total of eight outputs in the form of bar readings. Non-Gaussian likelihood functions are involved in the modeling and data fusion of those sensors. Under the assumption that the prior distribution is a Gaussian density or can be approximated by a Gaussian density, two methods are presented for approximating the posterior mean and variance. The Gaussian sum method first approximates the non-Gaussian likelihood function by a mixture of Gaussian components and then uses the Kalman filter formulae to compute the posterior mean and variance. The Gaussian-Hermite method computes the posterior mean and variance through three integrals defined over infinite intervals and approximated by Gaussian-Hermite quadrature.


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    Titel :

    Bayesian Estimation for CBRN Sensors with Non-Gaussian Likelihood


    Beteiligte:
    Yang Cheng, (Autor:in) / Konda, U (Autor:in) / Singh, T (Autor:in) / Scott, P (Autor:in)


    Erscheinungsdatum :

    2011-01-01


    Format / Umfang :

    2336029 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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