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.
Bayesian Estimation for CBRN Sensors with Non-Gaussian Likelihood
IEEE Transactions on Aerospace and Electronic Systems ; 47 , 1 ; 684-701
2011-01-01
2336029 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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