Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan

Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets. References Nathan D Merchant, Kurt M Fristrup, Mark P Johnson, Peter L Tyack, Matthew J Witt, Philippe Blondel, and Susan E Parks. Measuring acoustic habitats. Methods in Ecology and Evolution, 6(3):257–265, 2015. doi:10.1111/2041-210X.12330. Nathan D Merchant, Philippe Blondel, D Tom Dakin, and John Dorocicz. Averaging underwater noise levels for environmental assessment of shipping. The Journal of the Acoustical Society of America, 132(4):EL343–EL349, 2012. doi:10.1121/1.4754429. Chi-Fang Chen, Hsiang-Chih Chan, Ray-I Chang, Tswen-Yung Tang, Sen Jan, Chau-Chang Wang, Ruey-Chang Wei, Yiing-Jang Yang, Lien-Siang Chou, Tzay-Chyn Shin, et al. Data demonstrations on physical oceanography and underwater acoustics from the marine cable hosted observatory (macho). In OCEANS, 2012-Yeosu, pages 1–6. IEEE, 2012. doi:10.1109/OCEANS-Yeosu.2012.6263639. Sauda Sadaf P Yashaswini, Soumya Halagur, Fazil Khan, and Shanta Rangaswamy. A literature survey on ambient noise analysis for underwater acoustic signals. International Journal of Computer Engineering and Sciences, 1(7):1–9, 2015. doi:10.26472/ijces.v1i7.37. Shuguang Wang and Xiangyang Zeng. Robust underwater noise targets classification using auditory inspired time-frequency analysis. Applied Acoustics, 78:68–76, 2014. doi:10.1016/j.apacoust.2013.11.003. LG Weiss and TL Dixon. Wavelet-based denoising of underwater acoustic signals. The Journal of the Acoustical Society of America, 101(1):377–383, 1997. doi:10.1121/1.417983. Timothy Alexis Bodisco, Jason D'Netto, Neil Kelson, Jasmine Banks, Ross Hayward, and Tony Parker. Characterising an ecg signal using statistical modelling: a feasibility study. ANZIAM Journal, 55:32–46, 2014. doi:10.21914/anziamj.v55i0.7818. José Ribeiro-Fonseca and Luís Correia. Identification of underwater acoustic noise. In OCEANS'94.'Oceans Engineering for Today's Technology and Tomorrow's Preservation.'Proceedings, volume 2, pages II/597–II/602 vol. 2. IEEE. Linus YS Chiu and Hwei-Ruy Chen. Estimation and reduction of effects of sea surface reflection on underwater vertical channel. In Underwater Technology Symposium (UT), 2013 IEEE International, pages 1–8. IEEE, 2013. doi:10.1109/UT.2013.6519874. G.M. Wenz. Acoustic ambient noise in the ocean: spectra and sources. Thesis, 1962. doi:10.1121/1.1909155. Donald Ross. Mechanics of underwater noise. Elsevier, 2013. doi:10.1121/1.398685. Chris Drummond and Robert C Holte. Exploiting the cost (in) sensitivity of decision tree splitting criteria. In ICML, volume 1, 2000. Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, pages 973–978. Lawrence Erlbaum Associates Ltd, 2001. Chris Gillard, Alexei Kouzoubov, Simon Lourey, Alice von Trojan, Binh Nguyen, Shane Wood, and Jimmy Wang. Automatic classification of active sonar echoes for improved target identification. Douglas C Montgomery. Design and analysis of experiments. John wiley and sons, 2017. doi:10.1002/9781118147634. G Taguchi. Off-line and on-line quality control systems. In Proceedings of International Conference on Quality Control, 1978. Sheng-Ju Wu, Sheau-Wen Shiah, and Wei-Lung Yu. Parametric analysis of proton exchange membrane fuel cell performance by using the taguchi method and a neural network. Renewable Energy, 34(1):135–144, 2009. doi:10.1016/j.renene.2008.03.006. Genichi Taguchi. Introduction to quality engineering: designing quality into products and processes. Technical report, 1986. doi:10.1002/qre.4680040216. Richard Horvath, Gyula Matyasi, and Agota Dregelyi-Kiss. Optimization of machining parameters for fine turning operations based on the response surface method. ANZIAM Journal, 55:250–265, 2014. doi:10.21914/anziamj.v55i0.7865. Chuan-Tien Li, Sheng-Ju Wu, and Wei-Lung Yu. Parameter design on the multi-objectives of pem fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms. International Journal of Hydrogen Energy, 39(9):4502–4515, 2014. doi:10.1016/j.ijhydene.2014.01.034. Antoine Guisan, Thomas C Edwards Jr, and Trevor Hastie. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3):89–100, 2002. doi:10.1016/S0304-3800(02)00204-1. Sheng Chen, Colin FN Cowan, and Peter M Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on neural networks, 2(2):302–309, 1991. doi:10.1109/72.80341. Howard Demuth and Mark Beale. Neural network toolbox for use with matlab-user's guide verion 4.0. 1993. Janice Gaffney, Charles Pearce, and David Green. Binary versus real coding for genetic algorithms: A false dichotomy? ANZIAM Journal, 51:347–359, 2010. doi:10.21914/anziamj.v51i0.2776. Daniel May and Muttucumaru Sivakumar. Techniques for predicting total phosphorus in urban stormwater runoff at unmonitored catchments. ANZIAM Journal, 45:296–309, 2004. doi:10.21914/anziamj.v45i0.889. Chang-Xue Jack Feng, Zhi-Guang Yu, and Andrew Kusiak. Selection and validation of predictive regression and neural network models based on designed experiments. IIE Transactions, 38(1):13–23, 2006. doi:10.1080/07408170500346378. Yin-Ying Fang, Ping-Jung Sung, Kai-An Cheng, Meng Fan Tsai, and Chifang Chen. Underwater radiated noise measurement of ocean researcher 3. In The 29th Taiwan Society of Naval Architects and Marine Engineers Conference, 2017. Yin-Ying Fang, Chi-Fang Chen, and Sheng-Ju Wu. Analysis of vibration and underwater radiated noise of ocean researcher 3. In The 30th Taiwan Society of Naval Architects and Marine Engineers Conference, 2018. Det Norske Veritas. Rules for classification of ships new buildings– special equipment and systems additional class– part 6 chapter 24– silent class notation. Rules for Classification of Ships–Newbuildings, 2010. Underwater acoustics–quantities and procedures for description and measurement of underwater sound from ships-part 1–requirements for precision measurements in deep water used for comparison purposes. (ISO 17208-1:2012), 2012. Bureau Veritas. Underwater radiated noise, rule note nr 614 dt r00 e. Bureau Veritas, 2014. R.J. Urick. Principles of underwater sound, volume 3. McGraw-Hill New York, 1983. Lars Burgstahler and Martin Neubauer. New modifications of the exponential moving average algorithm for bandwidth estimation. In Proc. of the 15th ITC Specialist Seminar, 2002. Bishnu Prasad Lamichhane. Removing a mixture of gaussian and impulsive noise using the total variation functional and split bregman iterative method. ANZIAM Journal, 56:52–67, 2015. doi:10.21914/anziamj.v56i0.9316. Chao-Ton Su. Quality engineering: off-line methods and applications. CRC press, 2016. Jiju Antony and Mike Kaye. Experimental quality: a strategic approach to achieve and improve quality. Springer Science and Business Media, 2012. Ozkan Kucuk, Tayeb Elfarah, Serkan Islak, and Cihan Ozorak. Optimization by using taguchi method of the production of magnesium-matrix carbide reinforced composites by powder metallurgy method. Metals, 7(9):352, 2017. doi:10.3390/met7090352. G Taguchi. System of experimental design, quality resources. New York, 108, 1987. Gavin C Cawley and Nicola LC Talbot. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 36(11):2585–2592, 2003. doi:10.1016/S0031-3203(03)00136-5