Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions

Suo, Jianqin; Dong, Haitao; Shen, Xiaohong; Wang, Haiyan

doi:10.1016/j.chaos.2020.109825

Cited by 9 publications

(6 citation statements)

References 49 publications

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“…This work can effectively improve weak-signal detection performance, especially under low-SNR circumstances. Therefore, we further employed a particular parameter-tuning method with a classical bistable stochastic resonance model for vector acoustic DOA estimation, which can effectively improve AAIM estimation performance [20]. This work preliminarily demonstrated that preprocessing using bistable stochastic resonance can enhance vector acoustic DOA estimation under low-SNR conditions.…”

Section: Introductionmentioning

confidence: 89%

“…Accordingly, single UAVSs have been ubiquitously applied in small underwater platforms, such as gliders, ocean bottom seismometers (OBSs), subsea latent buoys, and other ocean-monitoring equipment [10][11][12][13]. As a consequence, a sizable amount of the literature has focused on the application of single UAVSs in recent years, mainly with respect to vector acoustic DOA estimation [3,4,[14][15][16][17][18][19][20][21][22]. From the point of view of methodology, there are three kinds of directions for single-vector acoustic DOA estimation, namely array signal processing-based methods and intensity-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Dong,

Suo,

Zhu

et al. 2024

Remote Sensing

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Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Dong,

Suo,

Zhu

et al. 2024

Remote Sensing

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“…SNR, traditionally used to describe the degree of signal and noise, can also measure the enhancement in SR. 29 The SNR including SNR i and SNR o is expressed in equation (2), which depends on A, D, a and b. The SR can be realized when the parameters are matched.where A(f 0 ) and A(f t ) are the amplitude of f and f t , and N is length of the time series.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Significantly, the moving frequency is determined by the Kramers transition rate r K =ω/π, while the weak periodic signal is strengthened and fault feature is recognized. SNR, traditionally used to describe the degree of signal and noise, can also measure the enhancement in SR. 29 The SNR including SNR i and SNR o is expressed in equation ( 2), which depends on A, D, a and b. The SR can be realized when the parameters are matched.…”

Section: Stochastic Resonancementioning

confidence: 99%

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Song

et al. 2022

Structural Health Monitoring

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The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.

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“…They have been ubiquitously applied in small underwater platforms such as gliders, ocean bottom seismome-ters(OBS), sub-sea latent buoys, and other ocean monitoring equipment [11][12][13][14]. As a consequence, in recent years a sizable literature has focused on the applications of single UAVS, mainly for vector acoustic DOA estimation [4,5,[15][16][17][18][19][20][21][22][23]. From the view of methodology, the single-vector acoustic DOA estimation methods can be classified into array signal processing-based and intensity-based methods.…”

Section: Introductionmentioning

confidence: 99%

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

Dong,

Suo,

Zhu

et al. 2024

Electronics

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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.

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Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions

Cited by 9 publications

References 49 publications

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

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