Tracking a swift target using the interacting fuzzy multi-model algorithm

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 sinusoidal. Decentralized tracking decisions are thus generated which are further evaluated by learning prediction module and are fused before being sent to the guidance module.