Abstract
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pawlak, Z.: Why Rough Sets? In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, vol. 2, pp. 738–743 (1996)
Petters, J.F., et al.: Rough Neural Computing in Signal Analysis. Computational Intelligence 17, 493–513 (2001)
Petters, J.F., et al.: Towards Rough Neural Computing Based on Rough Membership Functions. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 611–618. Springer, Heidelberg (2001)
Petters, J.F., et al.: Design of Rough Neurons. Rough Set Foundation And Petri Net Method. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 283–291. Springer, Heidelberg (2000)
Wu, Y., et al.: A Rough Neural Network for Material Proportioning System. In: IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions, vol. 2 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, Y., Ji, H., Gao, X. (2005). A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_61
Download citation
DOI: https://doi.org/10.1007/11427445_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)