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Estimation of efficiency of a tree structured hierarchical wavelet representation of synthetic database applied to non-cooperative target recognition

Published online by Cambridge University Press:  18 May 2011

Christian Brousseau*
Affiliation:
IETR, Université de Rennes 1, Campus de Beaulieu, 35042 Rennes Cedex, France. Phone: +33 2 2323 6231
*
Corresponding author: C. BrousseauEmail:[email protected]

Abstract

In this paper, problem of efficient representation of large database of target radar cross section (RCS) is investigated in order to minimize memory requirements and recognition search time, using a tree structured hierarchical wavelet representation. Synthetic RCS of large aircrafts, in the High Frequency (HF)–Very High Frequency (VHF) bands, are used as experimental data. Hierarchical trees are built using wavelet multiresolution representation and K-means clustering algorithm. Criteria used to define these hierarchical trees are described and the obtained performances are presented.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2011

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References

REFERENCES

[1]Baras, J.S.; Dey, S.: Adaptive classification based on compressed data using learning vector quantization, in Proc. 38th Conf. on Decision & Control, Phoenix, AZ, USA, 1999.Google Scholar
[2]Barès, C.; Brousseau, C.; Bourdillon, A.: A multifrequency HF–VHF radar system for aircraft identification, in IEEE Int. Radar Conf., Arlington, VA, USA, 2005.Google Scholar
[3]Brousseau, C.: Définition, réalisation et tests d'un radar VHF multifréquence et multipolarisation – project MOSAR. PhD Thesis, University of Rennes I, 1995.Google Scholar
[4]Burke, G.J.; Poggio, A.J.: Numerical electromagnetic code – method of moments, Part I: program description and theory. Technical Document 116, Naval Electronics Systems Command (ELEX 3041), 1977.Google Scholar
[5]David, A.; Brousseau, C.; Bourdillon, A.: Simulations and measurements of radar cross section of a Boeing 747–200 in the 20–60 MHz frequency band. Radio Sci., 38(4) (2003), 10641070.CrossRefGoogle Scholar
[6]Mallat, S.: A Wavelet Tour of Signal Processing, Academic Press, Paris, 1998.CrossRefGoogle Scholar
[7]Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11 (7) (1989), 674693.CrossRefGoogle Scholar
[8]MacQueen, J.B.: Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. on Mathematical Statistics and Probability, Berkeley, CA, USA, 1967.Google Scholar
[9]Zeng, Y.; Starzyk, J.: Statistical approach to clustering in pattern recognition, in Proc. 33rd Southeastern Symp. on System Theory, Athens, OH, USA, 2001.Google Scholar
[10]Duda, R.O.; Hart, P.E.; Stork, D.G.: Pattern Classification, John Wiley & Sons, 2nd ed., New York, 2001.Google Scholar
[11]Marques de Sà, J.P.: Pattern Recognition – Concepts, Methods and Applications, Springer Editions, 2001.Google Scholar
[12]Bezdek, J.C.; Dunn, J.C.: Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans. on Computers, 24 (1975), 835838.CrossRefGoogle Scholar
[13]Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybern., 3(3) (1973), 3257.CrossRefGoogle Scholar
[14]Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.CrossRefGoogle Scholar
[15]Brousseau, C.: Application of the multiresolution wavelet representation to non-cooperative target recognition, in Radar’09 – Int. Radar Conf., Bordeaux, France, 2009.Google Scholar
[16]Brousseau, C.: Estimation of efficiency of multiresolution approach applied to non-cooperative target recognition, in Radar2010 – IEEE Int. Radar Conf., USA, 2010.Google Scholar
[17]Cover, T.; Hart, P.: Nearest neighbour pattern classification. IEEE Trans. Inf. Theory, IT-13 (1) (1967), 2127.CrossRefGoogle Scholar