Pattern recognition techniques for classifying aeroballistic flying vehicle paths

Here is a task of classifying aeroballistic vehicle path in atmospheric phase. It has been suggested the task to be solved by means of pattern recognition technique involving tutor-assisted training with a variety of classification samples, i.e., flying vehicle paths in atmospheric phase targeted at different ground objects. The pattern recognition technique has been developed, which involves minimum distance to a class standard and artificial neural network such as multilayer perceptron for the task solution. The novelty of the developed technique includes generation of gliding aircraft path through simulation of spline approximation of sectional polyline defined with a set of fixed points which include origin and end of the path as well as disturbing points. The path recognition system quality is to be assessed through probability measures. The path recognition quality assessment during study of various recognition techniques involved calculations of probability measures for different values of class numbers, path types (with changing number and steepness of maneuvers), and time interval assigned for decision-making. There is a set of programs involving mathematical modeling of aircraft paths in atmospheric phase, recognition procedures based on minimum distance to class standard method and artificial neural network such as multilayer perceptron as well as recognition quality check program through statistical assessment of correct recognition probabilities. The developed set of programs is a basis for study of various aircraft path recognition techniques with different initial data. There are computer modeling results and calculations of aircraft path classification error probabilities using the mentioned techniques in case of various path class number and limitations of decision-making time.