Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms

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Abstract
The purpose of this paper is to describe an approach which performs data fusion on the output of multiple, spatially separate, sensors engaged in the real time tracking of obstacles in a helicopter's environment. The generated information can be used either as a flight director aid or as feedback required by an automatic collision avoidance system. Obstacle track estimation has been commonly carried out using the Kalman filter (KF) for linear estimation, or the extended Kalman filter (EKF) for use on nonlinear problems. However, certain assumptions made in the derivation of the EKF algorithms render it sub-optimal for aerial obstacle track estimation. Additionally, the EKF has problems with initialisation and divergence (stability) for many non-linear processes.Research at the University of Southampton has highlighted a link between fuzzy networks and associative memory neural networks. This link is important as it allows new learning rules to be developed for training fuzzy rules, and learning convergence to be proved. This paper explores methods of fusion of estimates using neuro-fuzzy models, and addresses some of the weakness of the Kalman filter approximation introduced by the assumptions made in its derivation.

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