Abstract
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.
Highlights
Weak signals are extremely weak and difficult to detect in most situations
In order to realize intelligence statistical detection and improve target signal extraction accuracy of weak pulse signals under chaotic noise, this paper proposes the phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model for detection and extraction of weak pulse signals in the context of chaotic noise
Aiming at the detection and extraction of weak pulse signals under strong chaotic noise, nonlinear characteristics of chaos and linear representation of pulse signals are combined to construct the phase space threshold autoregressive detection model (PTAR detection model) and double layer threshold autoregressive extraction model (DLTAR extraction model). e extraction model built in this paper is directly related to the detection model, adopts the same model structure, and does not require additional prior knowledge
Summary
Weak signals are extremely weak and difficult to detect in most situations. Signal-to-noise ratio (SNR) or signal-tointerference ratio (SIR) is a measure of relative strength of signal and noise [1, 2]. E detection and extraction of weak signals under chaotic noise interference can achieve lower SNR working thresholds by fully utilizing chaotic nonlinear characteristics of observation information [20, 21]. It has a wide range of applications in the fields of wireless secure communications, marine monitoring, and bioinformatics [22,23,24,25,26,27,28]. E structure of this paper is as follows: Section 2 explains weak pulse signal detection based on the PTAR model; Section 3 presents weak pulse signal extraction with the DLTAR model; Section 4 comprises simulation experiment results and analysis; Section 5 concludes the study
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