scholarly journals Detection and parameter estimation of weak pulse signal based on strongly coupled Duffing oscillators

2019 ◽  
Vol 68 (8) ◽  
pp. 080501
Author(s):  
Bao-Feng Cao ◽  
Peng Li ◽  
Xiao-Qiang Li ◽  
Xue-Qin Zhang ◽  
Wang-Shi Ning ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Pulse Signal ◽  
Strongly Coupled ◽  
Weak Pulse ◽  
Coupled Duffing Oscillators

scholarly journals Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Roc Curve ◽  
Linear Form ◽  
Hypothesis Test ◽  
Pulse Signal ◽  
Support Vector ◽  
Detector Performance ◽  
Threshold Autoregressive ◽  
Profile Least Squares ◽  
Area Under Roc Curve ◽  
Weak Pulse

With the development in communications, the weak pulse signal is submerged in chaotic noise, which is very common in seismic monitoring and detection of ocean clutter targets, and is very difficult to detect and extract. Based on the threshold autoregressive model, pulse linear form, Markov chain Monte Carlo (MCMC), and profile least squares (PrLS) algorithm, phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model are proposed for detection and extraction of weak pulse signals in chaotic noise, respectively. Firstly, based on noisy chaotic observation, phase space is reconstructed according to Takens’s delay embedding theorem, and the phase threshold autoregressive (PTAR) model is presented to detect weak pulse signals, and then the MCMC algorithm is applied to estimate parameters in the PTAR model; lastly, we obtain one-step prediction error, which is used to realize adaptively detection of weak signals with the hypothesis test. Secondly, a linear form for the pulse signal and PTAR model is fused to build a DLTAR model to extract weak pulse signals. The DLTAR model owns two kinds of parameters, which are affected mutually. Here, the PrLS algorithm is applied to estimate parameters of the DLTAR model and ultimately extract weak pulse signals. Finally, accurate rate (Acc), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) are used as the detector performance index; mean square error (MSE), mean absolute percent error (MAPE), and relative error (Re) are used as the extraction accuracy index. The presented scheme does not need prior knowledge of chaotic noise and weak pulse signals, and simulation results show that the proposed PTAR-DLTAR model is significantly effective for detection and extraction of weak pulse signals under chaotic interference. Specifically, in very low signal-to-interference ratio (SIR), weak pulse signals can be detected and extracted compared with support vector machine (SVM) class and neural network model.

Detection of weak pulse signal via stochastic resonance

2006 ◽  
Author(s):  
Mingyang Wang ◽  
Yiyu Zhou ◽  
Le Han ◽  
Wenli Jiang
Keyword(s):  
Stochastic Resonance ◽  
Pulse Signal ◽  
Weak Pulse

scholarly journals Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liyun Su ◽  
Meini Li ◽  
Shengli Zhao ◽  
Ting Xie
Keyword(s):  
Sensor Fusion ◽  
Risk Model ◽  
Hypothesis Test ◽  
Pulse Signal ◽  
Optimal Decision ◽  
Conditional Probability Density ◽  
Distributed Sensor ◽  
Local Linear ◽  
Weak Pulse ◽  
Fusion Detection

This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

scholarly journals Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liyun Su ◽  
Xiu Ling
Keyword(s):  
Neural Network ◽  
Pulse Signal ◽  
Least Square ◽  
Data Matrix ◽  
Support Vector ◽  
Model Parameters ◽  
Elman Neural Network ◽  
Useful Signal ◽  
Weak Pulse ◽  
Space Data

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

scholarly journals Statistical Detection of Weak Pulse Signal under Chaotic Noise Based on Elman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Signal Detection ◽  
Prediction Error ◽  
Pulse Signal ◽  
Learning Ability ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Elman Neural Network ◽  
Weak Pulse

Weak signal detection is a significant problem in modern detection such as mechanical fault diagnosis. The uniqueness of chaos and good learning ability of neural networks provide new ideas and framework for weak signal detection field. In this paper, Elman neural network is applied to detect and recover weak pulse signal in chaotic noise. For detection problem of weak pulse signal under chaotic noise, based on short-term predictability of chaotic observations, phase space reconstruction for observed signals is carried out. And Elman deep learning adaptive detection model (EDAD model) is established for weak pulse signal detection, and a hypothesis test is used to detect weak pulse signal from the prediction error. For the recovery of weak pulse signal under chaotic noise, a double-layer Elman deep neural network recovery model (DEDR model) is proposed, which is based on the Elman deep learning network model and single-point jump model for weak pulse signal, and it is optimized with goal of minimizing mean square prediction error of the Elman model. The profile least squares method is applied to estimate parameters of the DEDR model for difficult recovery of weak pulse signal because the DEDR model is essentially a semiparametric model with parametric and nonparametric parts. In the end, simulation experiments show that the model built in this paper can effectively detect and recover weak pulse signal in the background of chaotic noise.

scholarly journals Transient synchronization mutation of ring coupled Duffing oscillators driven by pulse signal

2011 ◽  
Vol 60 (10) ◽  
pp. 100509
Author(s):  
Wu Yong-Feng ◽  
Zhang Shi-Ping ◽  
Sun Jin-Wei ◽  
Peter Rolfe ◽  
Li Zhi
Keyword(s):  
Pulse Signal ◽  
Coupled Duffing Oscillators

scholarly journals Detection and estimation of weak pulse signal in chaotic background noise

2017 ◽  
Vol 66 (9) ◽  
pp. 090503
Author(s):  
Su Li-Yun ◽  
Sun Huan-Huan ◽  
Wang Jie ◽  
Yang Li-Ming
Keyword(s):  
Background Noise ◽  
Pulse Signal ◽  
Detection And Estimation ◽  
Weak Pulse
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