Interacting fuzzy multimodel intelligent tracking system for swift target manoeuvres

The objective of the paper is to generate the necessary subsystems for the intelligent guidance of an airborne vehicle locking onto a swift target. This paper focuses on the generation of an intelligent tracker module equipped with a wavelet based neural network that learns predictions from past experience. The perception of actual target manoeuvre and prediction of its future states are achieved in this work by "projecting" actual observations into decision spaces of local fuzzy predictions based on independent prototypical trajectory types: linear, parabolic and square root type trajectory. Decentralized tracking decisions are thus generated which are further evaluated by learning prediction module and are fused before being sent to the guidance module. The tracker is tested for 3 dimensional target tracking problem.