Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Abstract

Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

“…The method by Yan, Sun, Cheng, Kuai, and Zhang () uses a combination of resonance‐based sparse signal decomposition (RSSD) and Hilbert marginal spectrum analysis to recognize certain types of vessels, defined a priori. The authors use an RSSD decomposition of the original audio signal to separate it into high and low resonance signals, corresponding, respectively, to the target and background noise.…”

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

“…are implemented for extracting the features of an underwater acoustic signal. Additionally, countless time or/and frequency-domain feature extraction techniques like intrinsic time-scale decomposition [19], resonance-based sparse signal decomposition [20], etc., are used. Nevertheless, they failed to fully consider the structure features, resulting in significant problems like worst robustness and low recognition rate [21].…”