Detection and estimation of weak pulse signal in chaotic background noise

Abstract

As is well known, people has been suffering noise interference for a long time, and more and more researches show that a lot of weak signals such as pulse signal are embedded in the strong chaotic noise. The purpose of weak signal detection and recovery is to retrieve useful signal from strong noise. It is very difficult to detect and estimate the weak pulse signal which is mixed in the chaotic background interference. Therefore, the detection and recovery of weak signal are significant and have application value in signal processing area, especially for the weak pulse signal detection and recovery. By studying various methods of detecting and estimating the weak pulse signal in strong chaotic background noise, in this paper, we propose an efficient hybrid processing technique. First, based on the short-term predictability and sensitivity to the tiny disturbance, a new method is proposed, which can be used for detecting and estimating the weak pulse signals in chaotic background that the nonlinear mapping is unknown. We reconstruct a phase space according to Takens delay embedding theorem; then we establish the local linear autoregressive model to predict the short-term chaotic signal and obtain the fitting error, and judge whether there are weak pulse signals. Second, we establish a single-jump model for pulse signals, and combine the local linear autoregressive model with it to build a double local linear (DLL) model for estimating the weak pulse signal. DLL model contains two parameters, and the two parameters affect each other. We use the back-fitting algorithm to estimate model parameters and ultimately recover the weak pulse signals. Detecting and estimating the pulse signals in chaotic background turns into estimating the parameters of DLL model. The minimum fitting error criterion is used as the objective function to estimate the parameters of the DLL model. To make the estimation more exact, we can use the formula of mean square error. The new algorithm presented here in this paper does not need to know the prior knowledge of the chaotic background nor weak pulse signal, and this algorithm is also simple and effective. Finally, the simulation results show that the method is effective for detecting and estimating the weak pulse signals based on the chaotic background noise. Specifically, the weak pulse signal can be extracted well with low SNR and the minimum mean square error or the minimum normalized mean squared error is very low.

Weak Pulse Signal Distributed Fusion Detection with Multidimensional Nonlinear Correlation Subjected to Broad Learning

“…Regarding the detection of weak signals, it is mainly divided into the following types of methods: time-domain analysis/frequency-domain analysis and time-frequency domain analysis. In recent years, enormous different kinds of models, such as traditional statistical methods [18][19][20], neural network [21][22][23][24][25][26], Kalman filter [27][28][29][30][31][32], and (generalized) likelihood ratio (GLRT) [33,34], have been successfully applied to fusion detection in distributed sensor networks. Notably, Su et al [35,36] combined a local polynomial nonparametric statistical method with Bayesian risk for fusion detection under chaotic background and constructed a distributed sensor local linear autoregressive (DS-LLAR) model for obtaining the one-step prediction error of each sensor and the corresponding conditional probability density function under each sensor's hypothesis test.…”

Research on the Detection Method of MRS Signal Initial Amplitude Based on Chaotic Detection System

“…In 1992, Birx et al first proposed a method for weak signal detection using chaotic oscillators [28], and the nonlinear signal detection theory constructed by chaotic systems has received extensive attention. Many studies show that using chaotic systems to estimate parameters of unknown signals is superior to traditional signal identification methods in terms of accuracy and signal identification [29], [30], [31]. Del Marco et al identify human gait signals using accelerometer data and chaos detection methods in mobile devices [32], Jinfeng Hu et al used a chaotic detection system to identify weak periodic signals in the diagnosis of mechanical equipment, and the detection effect was better than the stochastic resonance (SR) method [33].…”

Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

“…The detection of weak signal in chaotic noise background is a new detection method based on catastrophe effect of nonlinear system, which can use less data to achieve lower SNR threshold under arbitrary noise background [5]. This method has become a research hotspot and an important branch of signal processing, and has a broad application prospect in communication, automation, fault diagnosis and seismic monitoring [6][7][8][9][10][11][12][13][14].…”

“…In the fields of communication, fault diagnosis, biomedicine and seismic monitoring, pulse signal is a typical signal form [15][16][17][18]. Improving the detection ability of weak pulse signal under noise background and accurately measuring pulse signal is of great significance for reducing the cost of equipment detection, providing theoretical ideas for the development of some high-precision detection instruments, finding early faults and enhancing the anti-interference ability of detection system [6]. Taken's theorem [19] provides a theoretical basis for the prediction of chaotic time series.…”