Abstract
Classically, automatic detection of targets in active sonar system is addressed by a matched filter processing followed by a constant False Alarm Rate (CFAR) thresholding method. Even though, various CFAR techniques viz. CA CFAR, GO CFAR and SO CFAR etc. are available in literature, none of them alone is sufficient to eliminate the false echoes. In certain applications, such as active sonar where the probability of false alarm pfa requirements are very stringent, the performance of CFAR alone cannot be used as detection criteria. Further, the choice of a particular CFAR algorithm is also a complex task, as the non-homogenous nature of the acoustic medium is difficult to predict. In this paper, a fusion algorithm is proposed for active sonar application where, in addition to CFAR technique, a support vector machines (SVM) based classification algorithm is also used to eliminate the false echoes. The performance of the algorithm is verified using practically measured data.
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
References
T.H. Glisson, C.I. Black, A.P. Sage, On sonar signal analysis. IEEE Trans. Aerosp. Electron. Syst. AES-6, 37–50 (1970)
B.O. Steenson, Detection performance of a mean-level threshold. IEEE Trans. Aerosp. Electron. Syst. AES-4, 529–534 (1968)
G.M. Dillard, Mean-level detection of nonfluctuating signals. IEEE Trans. Aerosp. Electron. Syst. AES-10, 795–799 (1974)
R.L. Mitchell, J.F. Walker, Recursive methods for computing detection probabilities. IEEE Trans. Aerosp. Electron. Syst. AES-7, 671–676 (1971)
R. Nitzberg, Analysis of the arithmetic mean CFAR normalizer for fluctuating targets. IEEE Trans. Aerosp. Electron. Syst. AES-14, 44–47 (1978)
J.D. Moore, N.B. Lawrence, Comparison of two CFAR methods used with square law detection of Swirling I targets, in Proceedings of the IEEE International Radar Conference, pp. 403–409 (1980)
M. Weiss, Analysis of some modified cell-averaging CFAR processors, in multiple-target situations. IEEE Trans. Aerosp. Electron. Syst. AES-18, 102–113 (1982)
V.G. Hansen, Constant false alarm rate processing in search radars, in Proceedings of the IEEE 1973 International Radar Conference, London, pp. 325–332 (1973)
V.G. Hansen, J.H. Sawyers, Detestability loss due to greatest of selection in a cell averaging CFAR. IEEE Trans. Aerosp. Electron. Syst. AES-16, 115–118 (1980)
G.V. Trunk, Range resolution of targets using automatic detectors. IEEE Trans. Aerosp. Electron. Syst. AES-14, 750–755 (1978)
P.P. Gandhi, S.A. Kassam, Analysis of CFAR processors in non-homogeneous Background. IEEE Trans. Aerosp. Electron. Syst. AES-24, 427–455 (1988)
M. Pontil, A. Verri, Support vector machines for 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intel. 20, 637–646 (1998)
V. Wan, W.M. Campbell, Support vector machines for speaker verification and identification, in Proceedings of IEEE Workshop Neural Networks for Signal Processing, Sydney, Australia, Dec 2000, pp. 775–784
E. Osuna, R. Freund, F. Girosi, Training support vector machines: application to face detection, in Proceedings of Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 130–136
T. Joachims, Transductive inference for text classification using support vector machines, in Presented at the International Conference on Machine Learning, Slovenia, June 1999
C.J. Burges, A tutorial on support vector machines for pattern recognition. Knowl. Discov. Data Mining 2, 121–167 (1998)
T. Evgenuiu, M. Pontil, Statistical Learning Theory: A Primer (1998)
B. Scholkopf, S. Kah-Kay, C.J. Burges, F. Girosi, P. Niyogi, T. Poggio, V. Vapnik, Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45, 2758–2765 (1997)
M. Ning, S.C. Chin, False alarm reduction by LS-SVM for manmade object detection from side scan sonar images. Senior Member IEEE, Member IEEE
D. He, H. Leung, CFAR Intrusion Detection Method Based on Support Vector Machine Prediction
Department of Electrical and Computer Engineering University of Calgary 2500 University Drive, NW Calgary, Alberta, Canada, T2N IN4
S.S. Salankar, B.M. Patre, SVM Based Model as an Optimal Classifier for the Classification of Sonar Signals
S. Jeong, S.-W. Ban, S. Choi, D. Lee, M. Lee, Surface Ship-Wake Detection Using Active Sonar and One-Class Support Vector Machine Sungmoon
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Naidu, C.C., Stalin, E.S. (2016). Performance of Fusion Algorithm for Active Sonar Target Detection in Underwater Acoustic Reverberation Environment. In: Satapathy, S., Rao, N., Kumar, S., Raj, C., Rao, V., Sarma, G. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 372. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2728-1_22
Download citation
DOI: https://doi.org/10.1007/978-81-322-2728-1_22
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2726-7
Online ISBN: 978-81-322-2728-1
eBook Packages: EngineeringEngineering (R0)