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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    Title :

    Bayesian Estimation for CBRN Sensors with Non-Gaussian Likelihood


    Contributors:
    Yang Cheng (author) / Konda, U (author) / Singh, T (author) / Scott, P (author)


    Publication date :

    2011-01-01


    Size :

    2336029 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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