|Table of Contents|

Citation:
　Jiao Zhang,Yaan Li,Wasiq Ali,et al.Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter[J].Journal of Marine Science and Application,2020,(1):148-154.[doi:10.1007/s11804-020-00122-w]
Click and Copy

Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter

Info

Title:
Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter
Author(s):
Jiao Zhang;  Yaan Li;  Wasiq Ali;  Lian Liu
Affilations:
Author(s):
Jiao Zhang;  Yaan Li;  Wasiq Ali;  Lian Liu
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Keywords:
Line spectrum enhancement; Kalman filter; Signal detection; Underwater acoustic signal; Frequency estimation
分类号:
-
DOI:
10.1007/s11804-020-00122-w
Abstract:
To detect weak underwater acoustic signals radiated by submarines and other underwater equipment, an effective line spectrum enhancement algorithm based on Kalman filter and FFT processing is proposed. The proposed algorithm first determines the frequency components of the weak underwater signal and then filters the signal to enhance the line spectrum, thereby improving the signal-to-noise ratio (SNR). This paper discussed two cases:one is a simulated signal consisting of a dual-frequency sinusoidal periodic signal and Gaussian white noise, and the signal is received after passing through a Rayleigh fading channel; the other is a ship signal recorded from the South China Sea. The results show that the line spectrum of the underwater acoustic signal could be effectively enhanced in both cases, and the filtered waveform is smoother. The analysis of simulated signals and ship signal reflects the effectiveness of the proposed algorithm.

References:

Abbate A, Koay J, Frankel J, Schroeder SC, Das P (1997) Signal detection and noise suppression using a wavelet transform signal processor:application to ultrasonic flaw detection. IEEE Trans Ultrason Ferroelectr Freq Control 44(1):14-26. https://doi.org/10.1109/58.585186
Casey TA (2007) National Defense Industrial Association distributed netted anti-submarine warfare systems study. J Acoust Soc Am 122(5):2998-2998. https://doi.org/10.1121/1.2942705
Chen W, Hou J, Sha L (2004) Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans Mob Comput 3(3):258-271. https://doi.org/10.1109/TMC.2004.22
Chen ZG, Li YA, Chen X (2015) Underwater acoustic weak signal detection based on Hilbert transform and intermittent chaos. Acta Phys Sin 64(20):73-80. (in Chinese). https://doi.org/10.7498/aps.64.200502
Coraluppi S, Carthel C, Wu C, Stevens M, Douglas J, Titi G, Luettgen M (2015) Distributed MHT with active and passive sensors. In:18th International Conference on Information Fusion (Fusion), Washington DC, pp 2065-2072
Cui G, Li H, Himed B (2014) A correlation-based signal detection algorithm in passive radar with DVB-T2 emitter. In:Conference on Signals, Systems & Computers. IEEE, Pacific Grove, pp 1418-1422. https://doi.org/10.1109/ACSSC.2014.7094695
Deng H, Liu Y, Cai H (2009) Time-varying UWA channel with Rayleigh distribution. Tech Acoust 28(2):109-112. (in Chinese). https://doi.org/10.3969/j.issn.1000-3630.2009.02.003
Detweiller C, Vasilescu I, Rus D (2007) An underwater sensor network with dual communications, sensing, and mobility. In:Oceans 2007-Europe. IEEE, Aberdeen, pp 1352-1357
He G, Cheng J, He X, You X, 2011. Detection of underwater target based on diagonal slice of fourth-order cumulant. 3rd International Workshop on Intelligent Systems and Applications, Wuhan, 1-4
DOI:https://doi.org/10.1109/isa.2011.5873271
Hovem JM (2007) Underwater acoustics:propagation, devices and systems. J Electroceram 19(4):339-347. https://doi.org/10.1007/s10832-007-9059-9
Izenman, Julian A (1988) An introduction to Kalman filtering with applications. Technometrics 30(2):243-244. https://doi.org/10.1080/00401706.1988.10488393
Kang B, Shin J, Song J, Choi H, Park P (2012) Acoustic source localization based on the extended Kalman filter for an underwater vehicle with a pair of hydrophones. World Acad Sci Eng Technol 6:10-25
Kazemi R, Farsi A, Ghaed MH, Karimi-Ghartemani M (2008) Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter. Signal Process 88(8):2114-2121. https://doi.org/10.1016/j.sigpro.2008.02.012
Kim D, Cano JC, Wang W, De Rango F, Hua K (2014) Underwater wireless sensor networks. Int J Distrib Sens Netw 10(4):1-2. https://doi.org/10.1155/2014/860813
Moradi MH, Ashoori RM, Baghbani KR (2014) ECG signal enhancement using adaptive Kalman filter and signal averaging. Int J Cardiol 173(3):553-555. https://doi.org/10.1016/j.ijcard.2014.03.128
Li G, Zhang B (2017) Novel method for detecting weak signal with unknown frequency based on Duffing oscillator. Chin J Sci Instrum 38(1):181-189. (in Chinese). https://doi.org/10.19650/j.cnki.cjsi.2017.01.024
Li Y, Chen Z, 2017. Entropy based underwater acoustic signal detection. 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad 656-660
DOI:https://doi.org/10.1109/IBCAST.2017.7868120
Luo B, Wang M, Wang S (2017) A highly efficient weak target linespectrum detection algorithm. Tech Acoust 36(2):171-176. (in Chinese). https://doi.org/10.16300/j.cnki.1000-3630.2017.02.013
Luo J, Han Y, Fan L (2018) Underwater acoustic target tracking:a review. Sensors 18(2):112. https://doi.org/10.3390/s18010112
Lv H, Cao Z, Yan X, Li P (2009) A combined method of weak signal detection. In:Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA), Wuhan, pp 106-109. https://doi.org/10.1109/paciia.2009.5406484
Pan J, Han J, Yang S (2010) A neural network based method for detection of weak underwater signals. J Mar Sci Appl 9(3):256-261. https://doi.org/10.1007/s11804-010-1004-7
Hu P, Gong S, Bao Z (2011) Detection of line spectrum of the ship shaftrate electric field based on the improved adaptive line enhancement. In:International Conference on Consumer Electronics, Communications and Networks (CECNet), Xianning, pp 256-259. https://doi.org/10.1109/CECNET.2011.5768747
Prince, Mehra R, Kumar R (2015) Improving the SNR of the underwater acoustic signal affected by wind driven ambient noise using RLS algorithm. In:International Conference on Soft Computing Techniques and Implementations (ICSCTI), Berkeley, pp 45-50.https://doi.org/10.1109/icscti.2015.7489536
Tufts TW, Griffiths LJ, Widrow B (1977) Adaptive line enhancement and spectrum analysis. Proc IEEE 65(1):169-173. https://doi.org/10.1109/PROC.1977.10445
Siddagangaiah S, Li Y, Guo X, Chen X, Zhang Q, Yang K, Yang Y (2016) A complexity-based approach for the detection of weak signals in ocean ambient noise. Entropy 18(3):101. https://doi.org/10.3390/e18030101
Su L, Ma H, Tang S (2007) Weak signal detection of strong fractional noise using chaos oscillator and Kalman filtering. Journal of Sichuan University:149-154(in Chinese)
Wang G, Chen D, Lin J, Chen X (1999) The application of chaotic oscillators to weak signal detection. IEEE Trans Ind Electron 46(2):440-444. https://doi.org/10.1109/41.753783
Wang H, Shi M (2013) Detection of line-spectrum of radiated noises from underwater target based on chaotic system. In:IEEE International Conference on Signal Processing, Communication and Computing(ICSPCC 2013), Kunming, pp 1-4. https://doi.org/10.1109/ICSPCC.2013.6663887
West NE, Harwell K, McCall B (2017) DFT signal detection and channelization with a deep neural network modulation classifier. In:IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Piscataway, pp 1-3. https://doi.org/10.1109/DySPAN.2017.7920745
Zhao S, Chen X, Sun C (2013) Applications of adaptive line-spectrum enhancement algorithm to the detection of low frequency linespectrum signal. Sci Technol Rev 31(11):56-59(in Chinese)

Memo

Memo:
Received date:2019-04-26;Accepted date:2019-07-05。
Foundation item:This paper is supported by the National Natural Science Foundation of China (No. 11574250, No. 11874302).
Corresponding author:Yaan Li,liyaan@nwpu.edu.cn
Last Update: 2020-07-24