Abstract
The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward's clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering validity indexes are used to determine the range of the optimal number of clusters. In order to narrow this scope and find the optimal number of classifications, a sufficient number of samples are selected in the vicinity of each class center to train probabilistic neural networks, which correspond to different number of classifications. Then, the classifier of the optimal probabilistic neural network is obtained by calculating the maximum value of classification validity index. Finally, the identification accuracy of the classifier is improved effectively by using the method of Bivariable correlation analysis. Simulation results also illustrate that the proposed algorithms can accurately identify the pulse emitter signals.
Highlights
Under the conditions of the rapid development of modern information technology, various types of communication equipment, such as radar and radio navigation equipment, radio and television equipment, and electronic computer and peripheral equipment, have been used by military and large technology companies
In the process of modeling, the multifunction radar (MFR) were considered as stochastic discrete event systems that communicated information by use of radar word level modeling, radar phrase level modeling, and radar sentence level modeling. e radar word was a fixed arrangement of finite number of pulses, the radar phrase was a series of limited number of radar words, and the radar sentence was a combination of limited number of radar phrases
The stochastic context-free grammar was a model for capturing the essential features of the MFR dynamics [2], it had some defects in estimating the parameters of stochastic context-free grammar. erefore, the expectation maximization algorithm had been proposed by LP Dai et al to estimate these parameters, which can be used to further estimate the characteristic parameters of MFR [3]
Summary
Under the conditions of the rapid development of modern information technology, various types of communication equipment, such as radar and radio navigation equipment, radio and television equipment, and electronic computer and peripheral equipment, have been used by military and large technology companies. Erefore, the expectation maximization algorithm had been proposed by LP Dai et al to estimate these parameters, which can be used to further estimate the characteristic parameters of MFR [3] From this point of view, the ultimate goal of the modeling technique based on syntax was to find the feature parameters of emitters. In [8], the authors had proposed an identification method of radar signals based on immune radial-basis function neural network, which can improve the convergence speed and performance of the algorithm. The method of determining the range of clustering number was subjective, which may reduce the identification accuracy of the algorithm To overcome this deficiency and further improve accuracy, a classification and identification scheme of emitter signals based on the Ward clustering method (WCM) and PNN with correlation analysis was proposed in this paper.
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