Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Laily Fajarwati(1), Yusron Feriadi(2), Endang Widjiati(3),

DOI: https://doi.org/10.15294/jte.v15i1.43878


(1) National Research and Innovation Agency
(2) National Research and Innovation Agency; Institut Teknologi Bandung
(3) National Research and Innovation Agency; Institut Teknologi Sepuluh Nopember

Abstract

With current developments, underwater communication using acoustic signals is widely used. Many things need to be prepared to support a reliable underwater communication system, such as taking measurements in a test tank to find out the correct measurement configuration. Underwater acoustic intensity measurements, which are detailed in this paper, are performed in the test tank using distance variation schemes. Measurements were made at various distances of 4, 10, 20, and 50 meters from the signal source. The hydrophone that was used has a sensitivity of -180 dB re 1V/µPa. The hydrophone was placed at a depth of 2 meters below the surface of the water in the test tank, which divided the test tank depth in half to ensure that reflections from the bottom and the surface were kept to a minimum. However, the problem is that there are noisy signals at different frequencies. This paper proposes a method using Noise Assisted - Multivariate Empirical Mode Decomposition (NA-MEMD) to decompose the signal and then calculate the sound intensity. The result shows that an increase in the distance between the transmitter and receiver, also causes a change in the intensity with an average change of 0.467 dB/meter. It is concluded that the NA-MEMD approach was shown to be successful in decomposing the intended signal from the noise to equalize the quality of the signal received at different distances, and the correlation between intensity value and change in distance is resilient, with a correlation value of 0.98, indicating a very strong correlation.

Keywords

hydrophone; NA-MEMD; signal decomposition; sound intensity; underwater acoustic

Full Text:

PDF

References

A. Zhou et al., “A Novel Noise-Aware Deep Learning Model for Underwater Acoustic Denoising,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–13, 2023, doi: 10.1109/TGRS.2023.3254652.

H. Yang, W. Shi, and G. Li, “Underwater acoustic signal denoising model based on secondary variational mode decomposition,” Def. Technol., Nov. 2022, doi: 10.1016/j.dt.2022.10.011.

Y. Fang, Q. He, L. Bai, H. Yu, S. Tian, and X. Wang, “A Multi-target Underwater Acoustic Signals Denoising Method Based on Wavelet,” in 2022 3rd International Conference on Electronics, Communications and Information Technology (CECIT), pp. 323–327, Dec. 2022, doi: 10.1109/CECIT58139.2022.00063.

Y. Song, F. Liu, and T. Shen, “A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network,” IET Commun., vol. 17, no. 2, pp. 135–144, Jan. 2023, doi: 10.1049/cmu2.12518.

B. Pranitha and L. Anjaneyulu, “Analysis of Underwater Acoustic Communication System Using Equalization Technique for ISI Reduction,” Procedia Comput. Sci., vol. 167, pp. 1128–1138, 2020, doi: 10.1016/j.procs.2020.03.415.

S. Jia, S. Zou, X. Zhang, D. Tian, and L. Da, “Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform,” Appl. Acoust., vol. 192, p. 108721, Apr. 2022, doi: 10.1016/j.apacoust.2022.108721.

T. Ma, G. Qiao, S. Liu, S. Mazhar, N. Zheng, and C. Pan, “Biologically inspired underwater acoustic communication based on discrete cosine transform,” Appl. Acoust., vol. 198, p. 108930, Sep. 2022, doi: 10.1016/j.apacoust.2022.108930.

P. Singh and D. Batham, “A Review of Underwater Communication Systems,” International Journal of Engineering Development and Research, vol. 10, issue 2, pp. 100-104, May. 2022, doi: 10.1729/Journal.30274.

S. Fattah, A. Gani, I. Ahmedy, M. Y. I. Idris, and I. A. Targio Hashem, “A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges,” Sensors, vol. 20, no. 18, p. 5393, Sep. 2020, doi: 10.3390/s20185393.

B. K. Iwana and S. Uchida, “An empirical survey of data augmentation for time series classification with neural networks,” PLoS One, vol. 16, no. 7, p. e0254841, Jul. 2021, doi: 10.1371/journal.pone.0254841.

N. ur Rehman and H. Aftab, “Multivariate Variational Mode Decomposition,” IEEE Trans. Signal Process., vol. 67, no. 23, pp. 6039–6052, Dec. 2019, doi: 10.1109/TSP.2019.2951223.

Wei, Li, Xu, and Huang, “A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery,” Entropy, vol. 21, no. 4, p. 409, Apr. 2019, doi: 10.3390/e21040409.

N. Saini, S. Bhardwaj, and R. Agarwal, “Classification of EEG signals using hybrid combination of features for lie detection,” Neural Comput. Appl., vol. 32, no. 8, pp. 3777–3787, Apr. 2020, doi: 10.1007/s00521-019-04078-z.

S. Sharma and S. Sen, “Structural damage detection in presence of temperature variability using 2D CNN integrated with EMD,” Struct. Monit. Maint., vol. 8, no. 4, pp. 379–402, 2021, doi: https://doi.org/10.12989/smm.2021.8.4.379.

X. Wang, A. Liu, Y. Zhang, and F. Xue, “Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network,” Remote Sens., vol. 11, no. 16, p. 1888, Aug. 2019, doi: 10.3390/rs11161888.

R. San-Segundo, M. Gil-Martín, L. F. D’Haro-Enríquez, and J. M. Pardo, “Classification of epileptic EEG recordings using signal transforms and convolutional neural networks,” Comput. Biol. Med., vol. 109, pp. 148–158, Jun. 2019, doi: 10.1016/j.compbiomed.2019.04.031.

C. Park, D. Looney, N. ur Rehman, A. Ahrabian, and D. P. Mandic, “Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 1, pp. 10–22, Jan. 2013, doi: 10.1109/TNSRE.2012.2229296.

T. A. Smith and J. Rigby, “Underwater radiated noise from marine vessels: A review of noise reduction methods and technology,” Ocean Eng., vol. 266, p. 112863, Dec. 2022, doi: 10.1016/j.oceaneng.2022.112863.

G. Li, W. Bu, and H. Yang, “Research on noise reduction method for ship radiate noise based on secondary decomposition,” Ocean Eng., vol. 268, p. 113412, Jan. 2023, doi: 10.1016/j.oceaneng.2022.113412.

Y. Li and L. Wang, “A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise, minimum mean square variance criterion and least mean square adaptive filter,” Def. Technol., vol. 16, no. 3, pp. 543–554, Jun. 2020, doi: 10.1016/j.dt.2019.07.020.

X. Lang, N. ur Rehman, Y. Zhang, L. Xie, and H. Su, “Median ensemble empirical mode decomposition,” Signal Processing, vol. 176, p. 107686, Nov. 2020, doi: 10.1016/j.sigpro.2020.107686.

L. Xiao, Z. Zhang, and J. Gao, “Ground Roll Attenuation of Multicomponent Seismic Data with the Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) Method,” Appl. Sci., vol. 12, no. 5, p. 2429, Feb. 2022, doi: 10.3390/app12052429.

Q. Zheng, T. Chen, W. Zhou, L. Xie, and H. Su, “Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions,” Biocybern. Biomed. Eng., vol. 41, no. 1, pp. 196–210, Jan. 2021, doi: 10.1016/j.bbe.2020.12.005.

Y. Zhang et al., “Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series,” J. Neural Eng., vol. 18, no. 4, p. 046018, Aug. 2021, doi: 10.1088/1741-2552/abecf2.

A. Alsalah, D. Holloway, M. Mousavi, and J. Lavroff, “Identification of wave impacts and separation of responses using EMD,” Mech. Syst. Signal Process., vol. 151, p. 107385, Apr. 2021, doi: 10.1016/j.ymssp.2020.107385.

X. Lurton, An Introduction to Underwater Acoustics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. doi: 10.1007/978-3-642-13835-5.

R. N. Tiwari, C. A. Niccolini Marmont Du Haut Champ, F. Reggio, P. Silvestri, A. Traverso, and M. L. Ferrari, “Acoustic signature analysis of a bladeless blower,” Appl. Acoust., vol. 208, p. 109382, Jun. 2023, doi: 10.1016/j.apacoust.2023.109382.

C. S. Tan, R. Mohd-Mokhtar, and M. R. Arshad, “Underwater acoustic distance measurement using fast fourier transform overlap,” 2017 IEEE 7th Int. Conf. Underw. Syst. Technol. Theory Appl. USYS 2017, vol. 2018-Janua, pp. 1–5, 2018, doi: 10.1109/USYS.2017.8309450.

B. Ma and T. Zhang, “An Analysis Approach for Multivariate Vibration Signals Integrate HIWO/BBO Optimized Blind Source Separation with NA-MEMD,” IEEE Access, vol. 7, pp. 87233–87245, 2019, doi: 10.1109/ACCESS.2019.2924272.

M. M. Dedović, S. Avdaković, N. Dautbašić, and A. Mujezinović, “ROCOF Estimation via EMD, MEMD and NA-MEMD,” Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, vol. 83, pp. 148–158, 2020, doi: 10.1007/978-3-030-24986-1_12.

X. Lang, Y. Zhang, L. Xie, X. Jin, A. Horch, and H. Su, “Use of Fast Multivariate Empirical Mode Decomposition for Oscillation Monitoring in Noisy Process Plant,” Ind. Eng. Chem. Res., vol. 59, no. 25, pp. 11537–11551, Jun. 2020, doi: 10.1021/acs.iecr.9b06351.

W. Ni, Y. Lou, X. Xu, Z. Shan, S. Xue, and W. Wang, “Seismic Random Noise Attenuation via Noise Assisted-multivariate EMD Based MSSA,” in 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE), pp. 1–5, Dec. 2022, doi: 10.1109/ICARCE55724.2022.10046637.

N. Dayong, S. Hongyu, X. Aoyu, G. Yongjun, D. Hongwei, and H. Jiaoyi, “Adaptive Noise Reduction Method of Synchronous Hydraulic Motor Acoustic Signal Based on Improved Dislocation Superposition Method,” IEEE Access, vol. 8, pp. 37161–37172, 2020, doi: 10.1109/ACCESS.2020.2975562.

J. C. Sanders et al., “Adherence to and Deviation from the Inverse-Square Law of Intensity for Sound and Light,” Phys. Teach., vol. 61, no. 5, pp. 374–377, May 2023, doi: 10.1119/5.0082472.

B. Jiang, X. Liu, and X. Wu, “Sound power estimation method based on dual arrays sound intensity scaling and sound pressure superposition,” Noise Control Eng. J., vol. 71, no. 1, pp. 24–33, Jan. 2023, doi: 10.3397/1/37713.

Refbacks

  • There are currently no refbacks.


Bookmark and Share