An Improved Cohen‐Class Based Extraction Method for Fine Spectral Feature of Line Spectrum From Ship‐Radiated Noise

ABSTRACT The line spectrum from ship‐radiated noise is a critical feature for passive sonar to detect underwater acoustic targets. However, due to weak target strength as well as severe propagation attenuation and oceanic ambient noise, the signals received by passive sonars generally manifest low signal‐to‐noise ratio (SNR), strong nonstationarity and overwhelmed Doppler‐shifted line spectrum. These challenges deteriorate the performance of conventional Cohen class time frequency distribution (CCTFD) methods in capturing the fine spectral feature of such signals. To overcome these difficulties, this research proposes an improved Cohen‐class method, termed ambiguity function‐instantaneous autocorrelation function joint filtering Wigner–Ville distribution (AIJF‐WVD). First, this study analyses how standard CCTFD's cross‐term suppression mechanism degrades time‐frequency resolution/concentration in time‐frequency distribution (TFD) when processing multicomponent Doppler‐shifted signals. Departing from conventional framework of cross‐term suppression via two‐dimensional low‐pass filtering along both frequency‐shift dimension and time‐delay dimension in ambiguity function (AF) domain, AIJF‐WVD presents a novel joint filtering approach that consists of designing one‐dimensional finite impulse response (FIR) filter solely along frequency‐shift dimension in AF domain (while maintaining time‐delay dimension unchanged) as well as subsequent one‐dimensional low‐pass filtering along time dimension in instantaneous autocorrelation function (IAF) domain based on the designed filter. Therefore, this novel method enhances TFD performance of cross‐term suppression and frequency resolution simultaneously while maintaining low computational complexity. Then, the performances of various CCTFD methods are quantitatively assessed using mean structural similarity (MSSIM), normalised Rényi entropy (NRE), half‐power bandwidth (HBW) and mean runtime. Finally, the global spectral estimation accuracy of Doppler‐shifted tonals is evaluated through tracking deviation analysis. Compared to conventional CCTFDs, AIJF‐WVD exhibits superior robustness and adaptability in low‐SNR background noise as evidenced by processing both simulated signals and ship‐radiated noise from sea trials. Furthermore, the refined approach is also validated to significantly improve cross‐term suppression, time‐frequency concentration and computational efficiency characteristics while preserving frequency resolution and superior tonal trajectory tracking capability for passive sonar.

Authors

Institutions

Publication Details

Journal
IET Radar Sonar & Navigation
Published
2025-01-01
DOI
https://doi.org/10.1049/rsn2.70083
Primary Topic
Underwater Acoustics Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Improved Cohen‐Class Based Extraction Method for Fine Spectral Feature of Line Spectrum From Ship‐Radiated Noise

Qi‐Jun Liu, Weiguo Dai, Shilin Sun, Yichuan Wang et al.
IET Radar Sonar & Navigation
Underwater Acoustics Research
article

An Improved Cohen‐Class Based Extraction Method for Fine Spectral Feature of Line Spectrum From Ship‐Radiated Noise

Qi‐Jun Liu, Weiguo Dai, Shilin Sun, Yichuan Wang, Zhiyan Li
article en

Abstract

ABSTRACT The line spectrum from ship‐radiated noise is a critical feature for passive sonar to detect underwater acoustic targets. However, due to weak target strength as well as severe propagation attenuation and oceanic ambient noise, the signals received by passive sonars generally manifest low signal‐to‐noise ratio (SNR), strong nonstationarity and overwhelmed Doppler‐shifted line spectrum. These challenges deteriorate the performance of conventional Cohen class time frequency distribution (CCTFD) methods in capturing the fine spectral feature of such signals. To overcome these difficulties, this research proposes an improved Cohen‐class method, termed ambiguity function‐instantaneous autocorrelation function joint filtering Wigner–Ville distribution (AIJF‐WVD). First, this study analyses how standard CCTFD's cross‐term suppression mechanism degrades time‐frequency resolution/concentration in time‐frequency distribution (TFD) when processing multicomponent Doppler‐shifted signals. Departing from conventional framework of cross‐term suppression via two‐dimensional low‐pass filtering along both frequency‐shift dimension and time‐delay dimension in ambiguity function (AF) domain, AIJF‐WVD presents a novel joint filtering approach that consists of designing one‐dimensional finite impulse response (FIR) filter solely along frequency‐shift dimension in AF domain (while maintaining time‐delay dimension unchanged) as well as subsequent one‐dimensional low‐pass filtering along time dimension in instantaneous autocorrelation function (IAF) domain based on the designed filter. Therefore, this novel method enhances TFD performance of cross‐term suppression and frequency resolution simultaneously while maintaining low computational complexity. Then, the performances of various CCTFD methods are quantitatively assessed using mean structural similarity (MSSIM), normalised Rényi entropy (NRE), half‐power bandwidth (HBW) and mean runtime. Finally, the global spectral estimation accuracy of Doppler‐shifted tonals is evaluated through tracking deviation analysis. Compared to conventional CCTFDs, AIJF‐WVD exhibits superior robustness and adaptability in low‐SNR background noise as evidenced by processing both simulated signals and ship‐radiated noise from sea trials. Furthermore, the refined approach is also validated to significantly improve cross‐term suppression, time‐frequency concentration and computational efficiency characteristics while preserving frequency resolution and superior tonal trajectory tracking capability for passive sonar.

IET Radar Sonar & NavigationVol. 19(1)
United States Naval Academy (US)
Openalex Percentile: Top 72%
Underwater Acoustics Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.