Noise reduction method for chaotic signals based on dual-wavelet and spatial correlation

In this paper, an effective noise reduction method is proposed to remove Gaussian noise embedded in chaotic signals. The proposed method has two major steps: an optimal choice of wavelet decomposition scales and an estimation of the wavelet coefficients; the former is determined by the noise residual ratio based on dual-wavelet, whereas the latter is analyzed combining with the singular spectrum analysis and the spatial correlation theory. The chaotic signals generated by Lorenz model as well as the observed monthly series of sunspots are, respectively, applied for simulation analysis, the experimental results show that the performance of the proposed method is superior to that of other methods.