Detection and Estimation of Radiological Sources
Mahendra K. Mallick, Vikram Krishnamurthy, Ba‐Ngu Vo · 2014
This chapter considers several inferential problems regarding radiological sources, including estimation of point and distributed sources using a collection of static observers and source search and estimation using mobile observers. It presents the detection and localization of radiological materials that emit gamma rays, the highly penetrating electromagnetic radiations that can travel large distances through air. The chapter devotes to batch estimation of point radiological sources using measurements from a fixed network of sensors. The Bayesian approach to inference assumes that the unknown parameter of interest is a random variable. In this framework, inferential procedures are based on the posterior distribution, that is, the distribution of the parameter conditional on the observations. Bayesian estimation was favored because of the possibility of developing accurate and computationally efficient approximations of optimal Bayesian estimators for large dimensional parameters. The Renyi divergence is only one of a number of candidates for measuring the difference between two distributions.