Bayesian Estimation for CBRN Sensors with Non-Gaussian Likelihood Authors Cheng, Y., Konda, U., Singh, T., and Scott, P. Source
Automatica,
Abstract 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.
@article{Singh11_IEEEAES, Author = {Y. Cheng and U. Konda and T. Singh and P. Scott}, Journal = {IEEE Transactions on Aerospace and Electronic Systems}, Month = {Jan.}, Pages = {684-701}, Title = {Bayesian Estimation for CBRN Sensors with Non-Gaussian Likelihood}, Volume = {47}, Number = {1}, Year = {2011} } |