Underwater Target Recognition Based on Line Spectrum and Support Vector Machine

To increase the classification of underwater target recognition by ship radiated noise signals, the feature extraction method of line spectrum and classification algorithm of support vector machine (SVM) are adopted in the paper. The basic principles of power spectrum and SVM are briefly introduced at first. Then the signals of ship radiated are processed by the power spectrum estimation and the nonlinear least-square polynomial to fitting the continuous spectrum. The ultimate line spectrum is obtained through selecting peak and removes side lobe, and then extracts the feature of number and intensity of line spectrum from it. At last, the characteristic vector is established with normalization process and the signals are classified by SVM with parameter optimization. The simulation experiment results demonstrate that the ship radiated signals can be classified or recognized availably based on line spectrum feature and SVM. This paper presents a method of underwater target recognition and provides guidance for the research on multi-classes recognition algorithms. Keywords-line spectrum; feature extraction; support vector machine ; target recognition; algorithms