Enhancement detection of characteristic signal using stochastic resonance by adding a harmonic excitation

Hu, Niaoqing; Hu, Lei; Zhang, Xiaofei; Gu, Fengshou; Ball, Andrew

doi:10.1088/1742-6596/364/1/012046

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…SR conditions (42) where SR conditions can be obtained by classical theories and methods, including timescale matching conditions [40,43], stability conditions [42,44], threshold conditions [45], amplitude gain under weak noise limit conditions [46], etc. Assume a periodic forcing signal is applied to the particle in a bistable QDW potential; the matched relationship of the signal frequency ( f 0 ), noise intensity (D), and nonlinear system parameters (a and b) can be obtained by maximizing the system SNRI.…”

Section: Theory Of Matched Stochastic Resonance (Msr)mentioning

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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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: Theory Of Matched Stochastic Resonance (Msr)mentioning

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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show abstract

“…SR conditions (20) where SR conditions can be obtained by classical theories and methods, including timescale matching conditions [28,39], stability conditions [52,53], threshold conditions [54], amplitude gain under weak noise limit conditions [55], etc. Assume a periodic forcing signal with an amplitude A and a frequency f 0 is applied to the particle with an overdamped first-order bistable QDW potential.…”

Section: Snri Stmentioning

confidence: 99%

Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise

Dong,

Ma,

Suo

et al. 2024

Sensors

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Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with α=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to α=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.

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A Weak Signal Detection Method Based on Stochastic Resonances and Its Application to the Fault Diagnosis of Critical Mechanical Components

Fan

et al. 2014

Through-Life Engineering Services

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Enhancement detection of characteristic signal using stochastic resonance by adding a harmonic excitation

Cited by 5 publications

References 10 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

Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise

A Weak Signal Detection Method Based on Stochastic Resonances and Its Application to the Fault Diagnosis of Critical Mechanical Components

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