Article details

Title: Development of Multiresolution Hierarchical Trees to Non-Cooperative Target Recognition
Author(s): Christian Brousseau               

Abstract: In this paper, the problem of efficient representation of large databases of target radar cross sections 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 HF-VHF bands, are used as experimental data. Hierarchical trees are built using wavelet multiresolution representation and K-means clustering algorithm. The criteria used to define these hierarchical trees are described and the obtained performances are presented.

Keywords: radar, target recognition, multiresolution, wavelet, clustering, hierarchical tree.

References:

[1] D.E. NELSON, J.A. STARZYK, D.D. ENSLEY – Wavelet Transformation and Signal Discrimination for HRR Radar Target Recognition, Multidimensional Systems and Signal Processing, Vol. 14, No. 1-3, pp. 9-24, Jan.-Jul. 2003
[2] E.J. ROTHWELL, K.M. CHEN, D.P. NYQUIST, J.E. ROSS, R. BEBERMEYER – A Radar Target Discrimination Scheme Using the Discrete Wavelet Transform for Reduced Data Storage, IEEE Transactions on Antennas and Propagation, Vol. 42, No. 7, pp. 1033-1037, Jul. 1994
[3] J.S. BARAS, S. DEY – Adaptive Classification Based on Compressed Data Using Learning Vector Quantization, Proc. of the 38th IEEE Conference on Decision and Control, Vol. 4, pp. 3677-3683, Phoenix, AZ, Dec. 7-10, 1999
[4] A. DAVID, C. BROUSSEAU, A. BOURDILLON – Validation of Heavy Aircraft RCS Simulations at Very High Frequencies, Proc. of the 5th International Conference on Radar Systems, Radar ’99, Brest, France, May 17-21, 1999
[5] C. BARÈS, C. BROUSSEAU, A. BOURDILLON – A Multifrequency HF-VHF Radar System for Aircraft Identification, Proc. of the 2005 IEEE International Radar Conference, pp. 478-482, Arlington, VA, May 9-12, 2005
[6] C. BARÈS, C. BROUSSEAU, A.! BOURDIL LON – Aircraft Identification Using RCS Measurements in the Low VHF Band, Proc. of the International Conference on Radar Systems, Radar 2004, Toulouse, France, Oct. 19-21, 2004
[7] G.J. BURKE, A.J. POGGIO – Numerical Electromagnetic Code (NEC) – Method of Moments. Volume I: Program Description – Theory, Interaction Note 363, Lawrence Livermore Laboratory, Livermore, CA, Jul. 1977
[8] A. DAVID, C. BROUSSEAU, A. BOURDILLON – Simulations and Measurements of a Radar Cross Section of a Boeing 747-200 in the 20-60 MHz Frequency Band, Radio Science, Vol. 38, No. 4, pp. 1064-1070, 2003
[9] T. COVER, P. HART – Nearest Neighbor Pattern Classification, IEEE Transactions on Information Theory, Vol. 13, No. 1, pp. 21-27, Jan. 1967
[10] S. MALLAT – A Wavelet Tour of Signal Processing, 2nd Edition, Academic Press, San Diego, CA, 1999
[11] S.G. MALLAT – A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674-693, Jul. 1989
[12] C. BROUSSEAU – Application of the Multiresolution Wavelet Representation to Non-Cooperative Target Recognition, Proc. of the International Radar Conference – Surveillance for a Safer World, Radar 2009, Bordeaux, France, Oct. 12-16, 2009
[13] Y. ZENG, J. STARZYK – Statistical Approach to Clustering in Pattern Recognition, Proc. of the 33rd Southeastern Symposium on System Theory, pp. 177-181, Athens, OH, Mar. 18-20, 2001
[14] R.O. DUDA, P.E. HART, D.G. STORK – Pattern Classification, 2nd Edition, John Wiley & Sons, New York, NY, 2001
[15] J.P. MARQUES de SÁ – Pattern Recognition: Concepts, Methods, and Applications, Springer-Verlag, Berlin, Germany, 2001
[16] J.B. MACQUEEN – Some Methods for Classification and Analysis of Multivariate Observations, Proc. of the 5th Berkeley Symposium on Mathematical Statistics and P! robabili ty, Vol. 1, pp. 281-297, Berkeley, CA, 1967
[17] J.C. DUNN – A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, Vol. 3, No. 3, pp. 32-57, 1973
[18] J.C. BEZDEK – Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, 1981
[19] C. BROUSSEAU – Development of a Tree Structured Hierarchical Wavelet Representation of Synthetic Database to NCTR, Proc. of the 2010 IEEE International Radar Conference, pp. 368-373, Washington, DC, May 10-14, 2010
[20] C. BROUSSEAU – Development of Multiresolution Hierarchical Trees to Non-Cooperative Target Recognition, Proc. of the 9th International Conference on Communications, COMM 2012, pp. 51-54, Bucharest, Romania, Jun. 21-23, 2012