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2023 Vol.11, Issue 4 Preview Page

Research Article

Automatic Detection Method of Frequency Feature Information Through DEMON Spectrum Enhancement and Analysis

DEMON 스펙트럼 향상 및 분석을 통한 주파수 특징 정보 자동 검출 방법

Do Kyung Shin1, Young Dae Kim2

신도경1, 김영대2

1,2Chief Research Engineer, Maritime Surface Sonar System LIG Nex1
1,2엘아이지 넥스원 해양연구소 소나체계개발단 수석연구원
31 December 2023. pp. 11-27
PDF Citation
Abstract
수동 소나는 표적에서 발생 되는 방사 소음을 통해 표적을 탐지 및 식별하기 위해 활용되는 센서이다. 표적의 방사 소음은 크게 기계류에 의한 소음, 유체역학에 의한 소음 및 프로펠러에 의한 소음으로 분류할 수 있으며, 본 논문에서는 프로펠러의 특성을 검출하기 위해서 DEMON 그램을 이용하여 주요 특징 성분을 검출한다. DEMON 그램의 주요 소음원은 프로펠러 소음으로써, 우리는 DEMON 그램 주파수 라인 분석을 통해서 함정의 고유 특징정보인 프로펠러 축 회전률(PSR), 프로펠러 날개 수(NOB), 분당 프로펠러 회전 수(SRPM) 등을 추정할 수 있다. 하지만 수중의 낮은 SNR 환경으로 인해 시각적으로 주파수 라인을 추적하는 것은 어려운 문제이며, 음탐사의 개인 능력에 따라 분석 시간 소요 및 정확도의 편차가 존재한다. 본 논문에서는 DEMON 그램에서의 잡음을 감소시키고 함정의 프로펠러 정보를 추정할 수 있는 주파수 라인을 자동으로 검출하는 방안을 제안한다. 제안한 방법은 시각적으로 구별하기 어려운 어려운 표적에 대한 주파수 라인 검출의 정확도를 향상시키며, 운용자가 상황에 대한 정확한 판단을 할 수 있도록 도움을 줄 수 있다. 본 연구의 실험 결과, 제안하는 방법이 주파수 라인 검출 오차를 0으로 수렴시키는 것을 통해 높은 정확도를 확인할 수 있었다.
Passive sonar is a sensor used to detect and identify targets by utilizing the emitted acoustic noise from the targets. The radiated noise from targets can be categorized into three main types: machinery noise, hydrodynamic noise, and propeller noise. In this paper, we utilize the DEMON (Detection of Envelope Modulation on Noise) gram to detect key characteristic components for propeller identification. The primary source of noise in the DEMON gram is propeller noise, and through frequency line analysis of the DEMON gram, we can estimate important target-specific features, such as Propeller Shaft Rate (PSR), Number of Blades (NOB), and Shaft Revolution Per Minute (SRPM). However, visually tracking frequency lines is difficult in underwater environments with low SNR (Signal-to-Noise Ratio), and analysis time and accuracy depend on the operator’s expertise. This paper proposes a solution to reduce noise in the DEMON gram and automatically detect frequency lines for estimating propeller information of the target submarine. The proposed method enhances the accuracy of frequency line detection for targets that are difficult to distinguish visually and can assist operators in making precise judgments in critical situations. Experimental results demonstrate that the proposed method achieves a convergence of the frequency line detection error to zero, ensuring high accuracy in propeller feature estimation.
Keywords
DEMON Gram
Propeller Shaft Rate(PSR)
BR(Blades Rate)
NOB(Number of Blades)
SRPM(Shaft Revolution per Minute)
Frequency line detection
References
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  10. J. Ni, M. Zhao, C. Hu, G. Lv, and Z. Guo, "Ship Shaft Frequency Extraction based on Improved Stacked Sparse Denoising Auto-Encoder Network", Journal of Applied Sciences, Vol. 12, No. 18, p. 9076, 2022. 10.3390/app12189076
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  12. B. Deepa, M. Anoop, S. P. Vijayan, and K. A. Sooraj, "Performance Evaluation of the DEMON Processor for Sonar", 2022 IEEE Region 10 Symposium (TENSYMP), pp. 1-6, 2022. 10.1109/TENSYMP54529.2022.9864381 PMC9708513
  13. C. D. G. Reis, L. R. Padovese, and M. C. F. Oliveira, "Automatic Detection of Vessel Signatures in Audio Recordings with Spectral Amplitude Variation Signature", Methods in Ecology and Evolution, Vol. 10, No. 9, pp. 1501-1516, 2019. 10.1111/2041-210X.13245
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  1. K. W. Chung, A. Sutin, A. Sedunov, and M. Bruno, "DEMON Acoustic Ship Signature Measurements in an Urban Harbor", Advances in Acoustics and Vibration, pp. 1-13, 2011. 10.1155/2011/952798
  2. N. N. Moura, J. M. Seixas, and R. Ramos, "Passive Sonar Signal Detection and Classification based on Independent Component Analysis", Sonar Systems, pp. 93-104, 2011. 10.5772/18286
  3. Y. Cheng, J. Qiu, and Z. Liu, "Challenges and prospects of underwater acoustic passive target recognition technology", Journal of Applied Acoustics, Vol. 38, No. 4, pp. 653-659, 2019.
  4. L. Wang, Y. Wang, S, and Song, F. Li, "Overview of fibre optic sensing technology in the field of physical ocean observation", Frontiers in Physics, Vol. 9, pp. 558, 2021. 10.3389/fphy.2021.745487
  5. G. Kemper, D. Ponce, and J. Telles, "An Algorithm to Obtain Boat Engine RPM from Passive Sonar Signals Based on DEMON Processing and Wavelets Packets Transform", Journal of Electrical Engineering & Technology, Vol. 14, pp. 2505-2521, 2019. 10.1007/s42835-019-00260-4
  6. M.A.R. Hashmi, and R.H. Raza, "Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification", Journal of Electrical Engineering & Technology, Vol. 18, pp. 561-578, 2023. 10.1007/s42835-022-01167-3
  7. L. Li, S. Song, and X. Feng, "Combined LOFAR and DEMON Spectrums for Simultaneous Underwater Acoustic Object Counting and F0 Estimation", Journal of Marine Science and Engineering, Vol. 10, No. 10, p. 1565, 2022. 10.3390/jmse10101565
  8. A. Pollara, G. Lignan, L. Boulange, A. Sutin, and H. Salloum, "Specifics of DEMON Acoustic Signatures for Large and Small Boats", The Journal of the Acoustical Society of America, Vol. 141, No. 5, p. 3991, 2017. 10.1121/1.4989137
  9. M. Song, J. Li, and J. Hui, "Extraction of Shaft Frequency based on the DEMON Line Spectrum", The Journal of the Acoustical Society of America, Vol. 144, p. 1944, 2018. 10.1121/1.5068505
  10. J. Ni, M. Zhao, C. Hu, G. Lv, and Z. Guo, "Ship Shaft Frequency Extraction based on Improved Stacked Sparse Denoising Auto-Encoder Network", Journal of Applied Sciences, Vol. 12, No. 18, p. 9076, 2022. 10.3390/app12189076
  11. K. Ma, Z. Chen, Y. Wang, and Y. Cheng, "An Automatic detection Algorithm for Multi-target Modulation Spectrum Shaft Frequency Under Low Signal-to-Noise Ratio", Journal of Vibration and Shock, Vol. 41, No. 24, pp. 19-26, 2022. 10.1177/14613484221104627
  12. B. Deepa, M. Anoop, S. P. Vijayan, and K. A. Sooraj, "Performance Evaluation of the DEMON Processor for Sonar", 2022 IEEE Region 10 Symposium (TENSYMP), pp. 1-6, 2022. 10.1109/TENSYMP54529.2022.9864381 PMC9708513
  13. C. D. G. Reis, L. R. Padovese, and M. C. F. Oliveira, "Automatic Detection of Vessel Signatures in Audio Recordings with Spectral Amplitude Variation Signature", Methods in Ecology and Evolution, Vol. 10, No. 9, pp. 1501-1516, 2019. 10.1111/2041-210X.13245
  14. A. Pollara, A. Sutin and H. Salloum, "Improvement of the Detection of Envelope Modulation on Noise (DEMON) and its application to small boats", OCEANS 2016 MTS/IEEE Monterey, Monterey, pp. 1-10, 2016. 10.1109/OCEANS.2016.7761197
  15. M. Üstündağ, A. Fatih, M Şengür, Gökbulut and A. Fikret, "Performance comparison of wavelet thresholding techniques on weak ECG signal denoising", Przegląd Elektrotechniczny, pp. 63-66, 2013.
  16. N. Yoder, "Peakfinder: Quickly finds local maxima (peaks) or minima(valleys) in a noisy signal", 2014.
  17. Y. Li, Z. Li, and Q.Qiu, "Assisting fuzzy offline handwriting recognition using recurrent belief propagation", IEEE Symposium Series on Computational Intelligence(SSCI), pp. 1-8, 2016. 10.1109/SSCI.2016.7850026
Information
  • Publisher :The Society of Convergence Knowledge
  • Publisher(Ko) :융복합지식학회
  • Journal Title :The Society of Convergence Knowledge Transactions
  • Journal Title(Ko) :융복합지식학회논문지
  • Volume : 11
  • No :4
  • Pages :11-27
  • DOI :https://doi.org/10.22716/sckt.2023.11.4.032
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