Realising the decomposition of a multi‐frequency signal under the coloured noise background by the adaptive stochastic resonance in the non‐linear system with periodic potential

Huang, Xiaogang; Zhang, Jingling; Lv, Meilei; Shen, Gang; Yang, Jianhua

doi:10.1049/iet-spr.2017.0532

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…The above theory assumes that the frequency of the signal to be detected is extremely low and the amplitude is extremely small. Therefore, the classical stochastic resonance theory can only deal with low-frequency signals with small amplitude, generally handle no more than a few Hz [28], [32]. But in actual scientific research and project application, center frequency of signal is greater than 1 and frequency band is wide.…”

Section: Signal Processing Flow Based Ontochastic Resonance Of Scale Change a Scale Change Model And Demonstrationmentioning

confidence: 99%

“…Literature s [30], [31] designed different detectors based on the statistical characteristics of noise. Literature [32] combined traditional empirical mode decomposition (EMD) method with SR to achieve the decomposition of multi-frequency signals in colored noise. Literature [33] proposed a method combining adaptive bistable stochastic resonance and multi-scale noise tuning based on the noise characteristics to improve the detection ability for weak signals.…”

Section: Introductionmentioning

confidence: 99%

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Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

et al. 2020

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Given that signal is weakened to a certain extent in the process of noise suppression using mainstream method, and that new noise is introduced by signal processing system, causing the decrease of detection performance, to improve the performance of detection to BPSK signal under the condition of strong noise and no prior information, the detection algorithm of BPSK signal of bistable stochastic resonance model based on scale change is proposed in this study. Using classical bistable stochastic resonance (BSR) system, only low-amplitude and low-frequency periodic signal can be processed. Scale change is first made to BSR in this study, verifying that BSR can be applied to high-frequency BPSK signal under high sampling frequency condition, and nonlinear threshold detection system is designed following Neyman-Pearson criterion to deduce and quantitatively show error rate of detector. Besides, complete flow for signal detection was built by taking it as feedback quantity to adjust the system parameters adaptively. Scale change feasibility and applicability of algorithm proposed in this study were verified through simulation experiment, which lays the theoretical basis for the detection of weak BPSK signal under low signal-to-noise ratio (SNR).

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Section: Signal Processing Flow Based Ontochastic Resonance Of Scale Change a Scale Change Model And Demonstrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

et al. 2020

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“…In this case, it is more appropriate to consider the colored noise, such as the exponentially correlated colored noise [18]. Scholars have also studied the problem of signal extraction in the background of colored noise [19][20][21][22]. In addition, mechanical faults sometimes do not exist in the form of single fault.…”

Section: Introductionmentioning

confidence: 99%

Extracting weak multi-frequency signal in heavy colored noise

Yang

Zhang

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

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“…These basic denoising methods have their own limitations, and cannot get ideal signal denoising result generally. So a research hotspot is to combine them to get better performance nowadays [10]- [13]. On the other hand, introducing deep learning into signal processing [14]- [16] is another hot topic at present.…”

Section: Introductionmentioning

confidence: 99%

Signal Denoising Based on Duffing Oscillators System

Luo¹,

Cui

2020

IEEE Access

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Signal denoising is an important aspect of signal processing. It is a meaningful direction to solve this problem by using chaotic oscillator. As a kind of chaotic oscillator, Duffing oscillator is often used in the field of periodic signal or pulse signal detection, but it has not been used in signal denoising. In this paper, a new Duffing oscillators system for signal denoising is proposed. The system utilizes the coupling between linear and nonlinear restoring forces of the oscillators to achieve complete synchronization of the system. Because the external signal will cause a large deviation of the oscillator's trajectory, while the Gauss white noise will only produce a small deviation. Based on this phenomenon, signal denoising can be realized. The simulation results show that, compared with the existing technology, our method is effective for the denoising of complex irregular signal, and can recover the mutation points of signal well. This method has less computational complexity and is easy to implement parallel computation. It can be used in real-time signal processing.

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Realising the decomposition of a multi‐frequency signal under the coloured noise background by the adaptive stochastic resonance in the non‐linear system with periodic potential

Cited by 9 publications

References 38 publications

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

Extracting weak multi-frequency signal in heavy colored noise

Signal Denoising Based on Duffing Oscillators System

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