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 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.

Tree-structured sensor fusion architecture for distributed sensor networks

1990 ◽  
Author(s):  
S. Sitharama Iyengar ◽  
Rangasami L. Kashyap ◽  
Rabinder N. Madan ◽  
Daryl D. Thomas
Keyword(s):  
Sensor Networks ◽  
Sensor Fusion ◽  
Distributed Sensor Networks ◽  
Distributed Sensor

Multi-Sensor N-P Criterion Fusion Detection Based on Weighting by SNR

2014 ◽  
Vol 1044-1045 ◽  
pp. 818-824
Author(s):  
Bo Fan Yang ◽  
Rui Wang ◽  
Gang Wang ◽  
Li Zhao
Keyword(s):  
Signal Detection ◽  
False Alarm ◽  
Sensor Fusion ◽  
Detection Probability ◽  
Detection System ◽  
False Alarm Probability ◽  
Radar Target ◽  
Low Snr ◽  
Simulation Results ◽  
Fusion Detection

Aiming at signal detection of radar target, concerning about on the basis of the influence of SNR on detection probability when false alarm probability is given based on N-P criterion, a kind of multi-sensor fusion detection based on SNR is put forward. It can improve system’s detection probability under the condition of required false alarm probability in the detection of low SNR signal. The simulation results show that the detection performance is significantly increased, no matter fusion detection system is composed of same sensors working in the same working point or different sensors.

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