Relative performance of selected detectors
Kenneth I. Ranney, Hiralal C. Khatri, Lam Hoang Nguyen, Jeffrey Sichina · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
The quadratic polynomial detector (QPD) and the radial basis function (RBF) family of detectors -- including the Bayesian neural network (BNN) -- might well be considered workhorses within the field of automatic target detection (ATD). The QPD works reasonably well when the data is unimodal, and it also achieves the best possible performance if the underlying data follow a Gaussian distribution. The BNN, on the other hand, has been applied successfully in cases where the underlying data are assumed to follow a multimodal distribution. We compare the performance of a BNN detector and a QPD for various scenarios synthesized from a set of Gaussian probability density functions (pdfs). This data synthesis allows us to control parameters such as modality and correlation, which, in turn, enables us to create data sets that can probe the weaknesses of the detectors. We present results for different data scenarios and different detector architectures.