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Iterative SVD method for noise reduction of low-dimensional chaotic time series

✍ Scribed by K. Shin; J.K. Hammond; P.R. White

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 201 KB
Volume: 13
Category: Article
ISSN: 0888-3270
DOI: 10.1006/mssp.1998.9999

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✦ Synopsis

A new simple method using singular value decomposition (SVD) is presented for reducing noise from a sampled signal where the deterministic signal is from a low-dimensional chaotic dynamical system. The technique is concerned particularly with improving the reconstruction of the phase portrait. This method is based on time delay embedding theory to form a trajectory matrix. SVD is then used iteratively to distinguish the deterministic signal from the noise. Under certain conditions, the method can be used almost blindly, even in the case of a very noisy signal (e.g. a signal to noise ratio of 6 dB). The algorithm is evaluated for a chaotic signal generated by the Duffing system, to which white noise is added.

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