ABSTRACT
Among the techniques used to eliminate a measurement noise disrupting a chaotic dataset, wavelet-based techniques are extremely efficient. However, a proper use of wavelets in the framework of a chaotic signal needs some adaptations from the classical linear case. Indeed, in the phase space, the impact of the noise on a chaotic system is nonlinear and the observations are non-equally spaced. These two specific features impose particular wavelet-based denoising rules, which are strongly related on how the wavelet coefficients are empirically computed. We present these denoising rules in the present chapter.
