Abstract
This paper presents a design framework based on a centralized scalable architecture for effective simulated aerial threat perception. In this framework data mining and pattern classification techniques are incorporated. This paper focuses on effective prediction by relying on the knowledge base and finding patterns for building the decision trees. This framework is flexibly designed to seamlessly integrate with other applications.
The results show the effectiveness of selected algorithms and suggest that more the parameters are incorporated for the decision making for aerial threats; the better is our confidence level on the results. To delve into accurate target prediction we have to make decisions on multiple factors. Multiple techniques used together helps in finding the accurate threat classification and result in better confidence on our results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lederer, P.G.: An introduction to Radar Absorbent Materials (RAM), Great Britain, Royal Signals and Radar Establishment (1986)
Zdunek, A., Rachowicz, W.: Cavity Radar Cross Section Prediction. IEEE Transaction on Antennas and propagation 56(6) (2008)
Przemieniecki, J.S.: Critical technologies for national defense, US. American Institute of Aeronautics and Astronautics (1991)
Youssef, N.Y.: Radar Cross Section of Complex Targets. Proceedings of IEEEÂ 77(5) (1989)
Jarrett, P.: Faster, further, higher: leading-edge aviation technology since 1945, United Kingdom, Putnam Aeronautical Books (2002)
Stephen, W.Y., Marshall: A Novel approach for Automatic Aircraft detection. In: Proceedings of UK European Signal Processing Conference, Tampere, Finland, September 4-8 (2000)
Vasserot, T.P.: The Jet fighter Radar Cross Section. Proceedings of IEEE Transaction on Aerospace and Electronic Systems (1975)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)
Bishop, J., Horspool, R.N., Worrall, B.: Experience in integrating Java with C# and.NET. Concurrency and Computation: Practice and Experience 17, 663–680 (2005)
Jaimez, C., Lucas, S.: Web Objects in XML. Efficient and easy XML serialization of Java and C# objects, http://woxserializer.sourceforge.net/index.html
Dybdal, R.B.: Radar Cross Section Measurements, Aerospace Corp electronics research Lab (1986)
Han, J., Kumber, M.: Data Mining: Concepts and techniques. Morgan Kaufmann Publishers, Urbana-Champaign US (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Anwar-ul-Haq, M., Malik, A.W., Khan, S.A. (2010). Aerial Threat Perception Architecture Using Data Mining. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14292-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-14292-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14291-8
Online ISBN: 978-3-642-14292-5
eBook Packages: Computer ScienceComputer Science (R0)