Divide and conquer approach to improve performance on ATR systems

Abstract

In this paper different methods applied to the Automatic Target Recognition problem are studied. A database of High Range Resolution radar profiles of six kinds of aircrafts is used to study the performance of four classification methods: k-Nearest Neighbor method, Multilayer Perceptrons, Radial Basis Function Networks, and Support Vector Machines. Results obtained with these classifiers show a high correlation between two of the classes of targets that cause the majority of errors. We propose to split the task into two subtasks. A first one in which the classes of correlated targets are grouped in a single class, and a second one to distinguish between them. Different classifiers are studied to be applied to each subtask. Results demonstrate that Radial Basis Function Networks are very good classifiers for the main subtask, while Support Vector Machines are the best classification method, among the studied, to distinguish between the correlated targets.