Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms
- 19 November 2002
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1392-1397 vol.2
- https://doi.org/10.1109/icsmc.1995.537967
Abstract
The main objective of this paper is to present some algorithms to fuse information about obstacles, whose dynamics are a-priori unknown, in a helicopter's environment, provided by multiple spatially separate sensors. The fused information can then be used to help helicopters locate obstacles in hazardous conditions so that it can avoid them. Obstacle track estimation has been commonly carried out using the Kalman filter (KF), a linear estimator, or one of its variations. The extended Kalman filter, one such variation designed for use on non-linear problems, produces the best linear approximation to the object track. However certain assumptions made in the derivation of these algorithms render them suboptimal for aerial obstacle track estimation. Work produced by 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 the conditions under which convergence can be proved to be derived. This paper explores methods for the fusion of estimates using these neurofuzzy models, and also addresses some of the weaknesses of the Kalman filter approximation introduced by the assumptions made in its derivation.Keywords
This publication has 7 references indexed in Scilit:
- The efficacy of fuzzy representations of uncertaintyIEEE Transactions on Fuzzy Systems, 1994
- Fuzzy sets-a convenient fiction for modeling vagueness and possibilityIEEE Transactions on Fuzzy Systems, 1994
- Comments on "The efficacy of fuzzy representations of uncertainty"IEEE Transactions on Fuzzy Systems, 1994
- The probability monopolyIEEE Transactions on Fuzzy Systems, 1994
- Intelligent ControlWorld Scientific Series in Robotics and Intelligent Systems, 1993
- Kalman Filtering with Real-Time ApplicationsSpringer Series in Information Sciences, 1987
- Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View* *Research supported in part by NASA Grant NCC-2–275 and NSF Grants ECS-8209679 and IST-8320416.Published by Elsevier BV ,1986