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Since the low SNR environment, generally the modulation recognition rate of signal modulation type is not very high. In this paper, we studied an automatic recognition method of communication signal modulation type in the low SNR. According to analyze the signal entropy as the feature, three characteristics are selected, and the random forest is as the classifier, finally we get a high recognition-rate...
We proposed an algorithm using Rényi entropy to distinguish the M-QAM signals in this article. There is no requirement of prior information of the parameters of the signal, because the method we proposed making Wigner-Ville distribution time-frequency transform to signal, then calculate the Rényi entropy, and the last the theory of Dempster-Shafer evidence was used to classify the signals. The results...
Automated Modulation Classification (AMC) shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. A useful digital signal modulation recognition scheme inspired by the deep auto-encoder network is proposed in this investigation. In our proposed method, there are two deep auto-encoder networks. The system extracts the original features of...
This paper focuses on the design of dimensionality reduction based on Fisherface. We propose to apply the Fisherface algorithm in face recognition to automatic modulation recognition, and combine it with cyclic spectrum and k nearest neighbor classifier to realize the correct recognition of 9 kinds of modulation signals. Fisherface is an improved algorithm based on Fisher linear discriminant analysis,...
It is known that the features of the radio station vary with the signal to noise ratio (SNR) in a certain range which leads to the uncertainty of the radio station identification system. In this paper, we study the interval evidence recognizer by extending the individual features of the obtained radio from single value to interval and constructing the radio station feature database. Firstly, the range...
Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction...
The target feature is sensitive to the aspect angle of SAR observation, making the interpretation and target recognition of the SAR image difficult. The information acquired from a certain aspect angle is partial and incomplete, and the multi-aspect observations have the potential to improve the SAR performance in this aspect. Three topics of fine feature description of multi-aspect SAR observations...
Quadrature Amplitude Modulation (M-QAM) was developed to rapidly and automatically identify the modulation levels of digitally modulated signals at low signal-to-noise ratios (SNR). The method uses wavelet transforms with the optimal scale combining with manifold learning method to identify the modulation levels of the M-QAM signals. Simulation results show that when the SNR is not lower than − 22...
In this paper, we consider the design of the fusion rules for binary distributed detection systems in which the decisions made by the local sensors are sent to the fusion center (FC) over parallel flat-fading channels using on–off keying. For complexity concerns, we focus on the linear-combining fusion rules whose combining weights are chosen to maximize the deflection coefficient of the fusion statistic...
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