Research of underwater bottom object and reverberation in feature space

Abstract

The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform (FDWT) feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.

Keywords

underwater bottom object / pattern of reverberation / feature clustering / feature space / underwater object detection

References

[1] Abraham DA. Signal excess in K-distribution reverberation. IEEE Journal of Oceanic Engineering, 2003, 28(3): 526-536

[2] Andriy S, Jonathan G, Bamshad M, Robin B. Personalized recommendation in social tagging systems using hierarchical clustering. ACM Conference on Recommender Systems, New York, 2008, 259-266

[3] Bucaro JA, Houston BH, Saniga M. Broadband acoustic scattering measurements of underwater unexploded ordnance (UXO). Journal of the Acoustical Society of America, 2008, 123(2): 738-746

[4] D’Andrea LM, Fisher GL, Harrison TC. Cluster analysis of adult children of alcoholics. International Journal of Addictions, 1994, 29(5): 565-582

[5] Eddy WF, Mockus A, Oue S. Approximate single linkage cluster analysis of large data sets in high-dimensional spaces. Computational Statistics and Data Analysis, 1996, 23: 29-43

[6] Foley DH, Sammon JW. An optimal set of discriminant vectors. IEEE Transactions on Computers, 1975, 24(3): 281-289

[7] Harrison CH. The influence of propagation focusing on clutter statistics. IEEE Journal of Oceanic Engineering, 2010, 35(2): 175-184

[8] Jin Z, Yang J, Hu Z, Lou Z. Face recognition based on uncorrelated discriminant transformation. Pattern Recognition, 2001, 34(7): 1405-1416

[9] Li X, Li T, Xia Z. Feature extraction and fusion based on the characteristics of underwater targets. Journal of Harbin Engineering University, 2010, 31(7): 903-908

[10] Li X, Xie L, Qin Y. Underwater target feature extraction using Hilbert-Huang transform. Journal of Harbin Enginerring University, 2010, 30(5): 542-546

[11] Li X, Yang S. Extraction of features of underwater target. Journal of Harbin Engineering University, 2001, 22(1): 25-29

[12] Liu C, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing, 2002, 11(4): 467-476

[13] Lyons AP, Abraham DA. Statistical characterization of high-frequency shallow-water seafloor backscatter. Journal of the Acoustical Society of America, 1999, 106(3): 1307-1315

[14] Ren H, Chang Y. Feature extraction with modified fisher’s linear discriminant analysis. Proceedings of the SPIE, 2005, 5995: 56-62

[15] Theodoridis S, Koutroumbas K. Pattern recognition, 2008, (4th Edition), Waltham, USA: Academic Press, 682-690

[16] Waters ZJ, Simpson HJ, Sarkissian A. Bistatic, above-critical angle scattering measurements of fully buried unexploded ordnance (UXO) and clutter. Journal of the Acoustical Society of America, 2012, 132(5): 3076-3085

[17] Yang S. Theory of underwater sound propagation, 2009, Harbin, China: Harbin Engineering University Press, 123-129