According to the empirical mode decomposition (EMD) theory, a prediction method of support vector machine (SVM) is proposed based on particle swarm optimization. The ensemble EMD method is used to decompose the signal into some intrinsic mode function components which are taken as the input of the SVM to predict the data. All the predicted values are combined, and the weak signals submerged in chaos background, including the transient signal and periodic signal, are detected from the prediction error. Lorenz attractor and the data from the McMaster IPIX radar sea clutter database are used in the simulation. The results show that the proposed method can effectively detect the weak target from chaotic signal. When the signal-to-noise ratio is 102.8225 dB in the chaotic noise background, by using the new method the root mean square error can be reduced by five orders of magnitude, reaching 0.00000033092, while the conventional SVM can reach only 0.049 under the condition of -54.60 dB and the weak target detected in sea clutter has the harmonic characteristics, which shows the prediction model has a lower threshold and error.
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