Hierarchical Bayesian models applied to air surveillance radars

Evaluation tests for air surveillance radars are often formulated in terms of the probability to detect a target at a specified range. Statistical methods applied in these tests do not explore all data in a full probabilistic model, which is crucial when dealing with small samples. The collected data are arranged longitudinally, in different levels (altitude), indexed both in time and distance. In this context we propose the application of dynamic Bayesian hierarchical models as an efficient way to incorporate the complete data set. Markov Chain Monte Carlo methods (MCMC) are used to make inference and to evaluate the proposed models.