To improve the accuracy and reliability of modulation recognition at low signal to noise ratio (SNR) and few knowledge of signal parameter, the novel method based on the cyclic spectral feature and support vector machine(SVM) is presented. In the process of novel algorithms, the cyclic spectral analysis is used to realize the feature extract of the modulated signals, and the Eigenface method is used to reduce the amount of spectral coherence feature. Then, a new scheme of classification based on support vector machine is presented to classify the modulation signal. The experiment shows that the modulation classification accuracy of presented method is significantly improved at low SNR environment.
The target detection feasibility and performance based on the TF energy integration are analyzed in this paper. Assuming the phase of signal is known, the time-frequency(TF) energy signal is got through TF transform, and the energy along the instantaneous frequency(IF) curve is integrated, the TF energy is used for target detection. Analytical and experimental results show that TF energy integration method based the target detection performance increases almost 6dB as compared to non-coherent integration, the advantage of the TF energy integration detection is that the echo signal parameters aren’t needed to be known.
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