Abstract
A novel feature extraction method based on the energy cumulant of Choi-Williams distribution has been proposed, which copes with the complex electromagnetic environment and varied signal styles. The energy cumulant of Choi-Williams distribution has been calculated from the accumulations of each frequency sample value with different time samples. Preceding this procedure, the time frequency distribution via Choi-Williams distribution has been processed through base noise reduction. Henceforth, a simulation analysis based on the discriminability and recognition effect of the proposed features in a low SNR environment has been carried out. In the discriminability experiment, the kernel fuzzy C-means clustering algorithm has been employed for classifying the radar pulse modulated radiation source signals. In the recognition effect analysis, the deep belief network has been proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results demonstrate that the proposed feature extraction method is feasible and robust in radar emitter classification and recognition even at a low SNR.
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