Weak signal detection via stochastic resonance (SR) has attracted considerable attention in a wide range of research fields, especially under heavy background noise circumstances. In this paper, a second-order matched stochastic resonance (SMSR) method is proposed to further improve the signal-to-noise ratio of weak period signal. By selecting a proper damping factor in the regime of second-order parameter matched relationship, weak periodic signal, background noise, and nonlinear system can be matched in generating an enhanced output. The matched relationship is deduced in combining noise intensity optimization and signal frequency synchronization with duffing system in a mathematical way, and a normalized scale transformation is further carried out to make it accessible in detecting arbitrary high frequency signals. The numerical analysis and application verification are performed to confirm the validity and effectiveness of theoretical results, which indicate the proposed SMSR method is superior to the first-order parameter matched stochastic resonance in achieving a good band-pass filtering effect with a low-noise output as the driving frequency of received signal is not too small (≥0.1 Hz). Thus, the proposed method is beneficial to practical engineering weak signal processing and anticipates to be a potential novel technique for ship radiated line-spectrum detection.

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work.

Remote passive sonar detection is significant as it constitutes potential real-time monitoring of severe underwater threats. There is still a lack of an efficient approach to achieving weak ship signal detection with nonparametric and noninformation priors. We propose the relative multiscale change entropy (RMCE) method to tackle these problems that should be promising. It measures the similarity between the signal under test and the ambient noise by combining different time scale factors to perceive the small changes in the complexity of the ambient noise, which is led by the presence of the distant ship. The value of RMCE is demonstrated by simulation and applied to the actual recorded data. We analyze the distribution characteristics of relative multiscale entropy of the ambient noise data with and without ship noise, collected in the South China Sea. Then this article presents a Neyman–Pearson (N–P) criterion-based RMCE detector for ship signal detection in the marine environment. The results show that the RMCE method outperforms the narrowband energy detection (NBED) method considerably.