Using neural networks as part of a system to recognise formations of aircraft

This paper describes a technique for recognising formations of aircraft from data that has been gathered by a number of independent sensors, then fused together to form a single representation of the environment. The task of recognising formations is formulated as a 3-D deformable template matching problem. The amount and type of deformation allowable by each template is learned from noisy examples of the template, using probability density estimation techniques. We compare a simple neural network approach to probability density estimation to a classical statistical approach. A more elaborate density estimation scheme is then presented that has been developed using ideas from both the classical statistical and neural network fields. Results are presented for all three techniques on simulated real world data