DEMON 스펙트럼 향상 및 분석을 통한 주파수 특징 정보 자동 검출 방법 = Automatic Detection Method of Frequency Feature Information through DEMON Spectrum Enhancement and Analysis

다국어 초록 (Multilingual Abstract)

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.

참고문헌 (Reference)

1 A. Pollara, "Specifics of DEMON Acoustic Signatures for Large and Small Boats" 141 (141): 3991-, 2017

2 J. Ni, "Ship Shaft Frequency Extraction based on Improved Stacked Sparse Denoising Auto-Encoder Network" 12 (12): 9076-, 2022

3 M. Üstündağ, "Performance comparison of wavelet thresholding techniques on weak ECG signal denoising" 63-66, 2013

4 B. Deepa, "Performance Evaluation of the DEMON Processor for Sonar" 1-6, 2022

5 N. Yoder, "Peakfinder: Quickly finds local maxima (peaks) or minima(valleys) in a noisy signal"

6 N. N. Moura, "Passive Sonar Signal Detection and Classification based on Independent Component Analysis" 93-104, 2011

7 L. Wang, "Overview of fibre optic sensing technology in the field of physical ocean observation" 9 : 558-, 2021

8 Hashmi Muhammad Abdur Rehman ; Raza Rana Hammad, "Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification" 대한전기학회 18 (18): 561-578, 2023

9 A. Pollara, "Improvement of the Detection of Envelope Modulation on Noise(DEMON)and its application to small boats" 1-10, 2016

10 M. Song, "Extraction of Shaft Frequency based on the DEMON Line Spectrum" 144 : 1944-, 2018

11 K. W. Chung, "DEMON Acoustic Ship Signature Measurements in an Urban Harbor" 1-13, 2011

12 L. Li, "Combined LOFAR and DEMON Spectrums for Simultaneous Underwater Acoustic Object Counting and F0 Estimation" 10 (10): 1565-, 2022

13 Y. Cheng, "Challenges and prospects of underwater acoustic passive target recognition technology" 38 (38): 653-659, 2019

14 C. D. G. Reis, "Automatic Detection of Vessel Signatures in Audio Recordings with Spectral Amplitude Variation Signature" 10 (10): 1501-1516, 2019

15 Y. Li, "Assisting fuzzy offline handwriting recognition using recurrent belief propagation" 1-8, 2016

16 K. Ma, "An Automatic detection Algorithm for Multi-target Modulation Spectrum Shaft Frequency Under Low Signal-to-Noise Ratio" 41 (41): 19-26, 2022

17 Guillermo Kemper ; David Ponce ; Joel Telles ; Christian del Carpio, "An Algorithm to Obtain Boat Engine RPM from Passive Sonar Signals Based on DEMON Processing and Wavelets Packets Transform" 대한전기학회 14 (14): 2505-2521, 2019