SNR gain enhancement in a generalized matched filter using artificial optimal noise

Ren, Yuanqiang; Pan, Yan; Duan, Fabing

doi:10.1016/j.chaos.2021.111741

Cited by 9 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming the noise covariance matrix R is known and the filter, characterized by the impulse response h(t), is linear and time-invariant, the MF will be based on maximization of the output signal-to-noise ratio (SNR) (Turin 1960, Robey et al 1992, Jiang et al, 2012, Bazdresch 2018, Ren et al 2022:…”

Section: Definition 2 the Characteristic Datasets Cmentioning

confidence: 99%

Data synchronization for gas emission measurements from dairy cattle: A matched filter approach

Milkevych

Villumsen²,

Løvendahl³

et al. 2022

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Section: Definition 2 the Characteristic Datasets Cmentioning

confidence: 99%

Data synchronization for gas emission measurements from dairy cattle: A matched filter approach

Milkevych

Villumsen²,

Løvendahl³

et al. 2022

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

“…Although the normalized variable scaling achieves SR with large parameters, the same parameter values cause fixed barrier height, which makes the input signal unable to reach the optimal stochastic resonance output. (3) The conventional adaptive SR takes the SNR as the optimization objective [13], which has a good effect on simple simulation signals. However, the effect is unstable when used for the enhancement and reconstruction of complex measured signals, so the prior fault information can be used to improve it.…”

Section: Introductionmentioning

confidence: 99%

“…However, the effect is unstable when used for the enhancement and reconstruction of complex measured signals, so the prior fault information can be used to improve it. Previous SR signal enhancement methods in the literature have only partially addressed some of these shortcomings, such as the SNR index adopted in the literature [13] are limited to the application of simulated signals and cannot be adapted to specific engineering problems. To enhance the application of SR in the field of rotational mechanical signal enhancement, the method proposed in this paper will simultaneously address the shortcomings of SR application from three perspectives by combining the a priori physical information.…”

Section: Introductionmentioning

confidence: 99%

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Yongdong,

Junxian,

Yonggang

2024

IEEE Access

View full text Add to dashboard Cite

Aiming at the problem that the characteristic frequency amplitude of early bearing fault signals is weak and difficult to extract under the strong noise, a vibration signal enhancement method based on maximum overlapping wavelet packet transform (MODWPT) and adaptive stochastic resonance (SR) is proposed. Based on multiple single-component signals decomposed by MODWPT, a signal enhancement and reconstruction model of the ordinary variable scale SR system is constructed. At the same time, the whale optimization algorithm (WOA) algorithm is adopted to optimize the parameters of the SR system adaptively with the improved signal noise ratio (ISNR) index as the fitness function. Finally, a feature-enhanced version of the original signal is obtained and transformed into a time-frequency image dataset using a twodimensional wavelet transform, which can be input into the Resnet network for fault identification and classification. Based on a set of early fault data from IMS and marine bearing data, the significant effect of the proposed method on early fault signal feature enhancement is verified. By comparing the fault characteristic frequency and its frequency doubling before and after the enhancement of the envelope spectrum, the component noise component is suppressed, and the fault frequency amplitude is highlighted under the action of SR potential function. This method can effectively improve the fault recognition accuracy, which has a certain application prospect.

show abstract

“…Studies have shown that using SR in weak signal detection is effective, especially under low-SNR conditions [28][29][30][31][32]. From the comprehensive perspective of performance and cost, the suboptimal stochastic resonance method has more potential for application due to its simple and efficient design [33][34][35][36], and by utilizing the SR, the proposed method can achieve a better performance in comparison to matched filter [37,38]. Nevertheless, these work mainly realized SR by adding an appropriate noise level to maximize the output SNR.…”

Section: Introductionmentioning

confidence: 99%

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

Dong,

Ma,

Suo

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

SNR gain enhancement in a generalized matched filter using artificial optimal noise

Cited by 9 publications

References 47 publications

Data synchronization for gas emission measurements from dairy cattle: A matched filter approach

Data synchronization for gas emission measurements from dairy cattle: A matched filter approach

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

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

Contact Info

Product

Resources

About