Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter

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.

Keywords

Line spectrum enhancement / Kalman filter / Signal detection / Underwater acoustic signal / Frequency estimation

References

[1] Abbate A, Koay J, Frankel J, Schroeder SC, Das P. Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Trans Ultrason Ferroelectr Freq Control, 1997, 44(1): 14-26

[2] Casey TA. National Defense Industrial Association distributed netted anti-submarine warfare systems study. J Acoust Soc Am, 2007, 122(5): 2998-2998

[3] Chen W, Hou J, Sha L. Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans Mob Comput, 2004, 3(3): 258-271

[4] Chen ZG, Li YA, Chen X. Underwater acoustic weak signal detection based on Hilbert transform and intermittent chaos. Acta Phys Sin, 2015, 64(20): 73-80 in Chinese)

[5] 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

[6] 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

[7] Deng H, Liu Y, Cai H. Time-varying UWA channel with Rayleigh distribution. Tech Acoust, 2009, 28(2): 109-112 in Chinese)

[8] Detweiller C, Vasilescu I, Rus D. An underwater sensor network with dual communications, sensing, and mobility. Oceans 2007-Europe, 2007, Aberdeen: IEEE, 1352-1357

[9] 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

[10] Hovem JM. Underwater acoustics: propagation, devices and systems. J Electroceram, 2007, 19(4): 339-347

[11] Izenman, Julian A. An introduction to Kalman filtering with applications. Technometrics, 1988, 30(2): 243-244

[12] Kang B, Shin J, Song J, Choi H, Park P. Acoustic source localization based on the extended Kalman filter for an underwater vehicle with a pair of hydrophones. World Acad Sci Eng Technol, 2012, 6: 10-25

[13] Kazemi R, Farsi A, Ghaed MH, Karimi-Ghartemani M. Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter. Signal Process, 2008, 88(8): 2114-2121

[14] Kim D, Cano JC, Wang W, De Rango F, Hua K. Underwater wireless sensor networks. Int J Distrib Sens Netw, 2014, 10(4): 1-2

[15] Moradi MH, Ashoori RM, Baghbani KR. ECG signal enhancement using adaptive Kalman filter and signal averaging. Int J Cardiol, 2014, 173(3): 553-555

[16] Li G, Zhang B. Novel method for detecting weak signal with unknown frequency based on Duffing oscillator. Chin J Sci Instrum, 2017, 38(1): 181-189 in Chinese)

[17] 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

[18] Luo B, Wang M, Wang S. A highly efficient weak target line-spectrum detection algorithm. Tech Acoust, 2017, 36(2): 171-176 in Chinese)

[19] Luo J, Han Y, Fan L. Underwater acoustic target tracking: a review. Sensors, 2018, 18(2): 112

[20] 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

[21] Pan J, Han J, Yang S. A neural network based method for detection of weak underwater signals. J Mar Sci Appl, 2010, 9(3): 256-261

[22] Hu P, Gong S, Bao Z (2011) Detection of line spectrum of the ship shaft-rate 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

[23] 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

[24] Tufts TW, Griffiths LJ, Widrow B. Adaptive line enhancement and spectrum analysis. Proc IEEE, 1977, 65(1): 169-173

[25] Siddagangaiah S, Li Y, Guo X, Chen X, Zhang Q, Yang K, Yang Y. A complexity-based approach for the detection of weak signals in ocean ambient noise. Entropy, 2016, 18(3): 101

[26] 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)

[27] Wang G, Chen D, Lin J, Chen X. The application of chaotic oscillators to weak signal detection. IEEE Trans Ind Electron, 1999, 46(2): 440-444

[28] 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

[29] 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

[30] Zhao S, Chen X, Sun C. Applications of adaptive line-spectrum enhancement algorithm to the detection of low frequency line-spectrum signal. Sci Technol Rev, 2013, 31(11): 56-59 (in Chinese)