4 research outputs found
Central Limit Theorems for Wavelet Packet Decompositions of Stationary Random Processes
This paper provides central limit theorems for the wavelet packet
decomposition of stationary band-limited random processes. The asymptotic
analysis is performed for the sequences of the wavelet packet coefficients
returned at the nodes of any given path of the -band wavelet packet
decomposition tree. It is shown that if the input process is centred and
strictly stationary, these sequences converge in distribution to white Gaussian
processes when the resolution level increases, provided that the decomposition
filters satisfy a suitable property of regularity. For any given path, the
variance of the limit white Gaussian process directly relates to the value of
the input process power spectral density at a specific frequency.Comment: Submitted to the IEEE Transactions on Signal Processing, October 200
Wavelet Packets of fractional Brownian motion: Asymptotic Analysis and Spectrum Estimation
International audienceThis work provides asymptotic properties of the autocorrelation functions of the wavelet packet coefficients of a fractional Brownian motion. It also discusses the convergence speed to the limit autocorrelation function, when the input random process is either a fractional Brownian motion or a wide-sense stationary second-order random process. The analysis concerns some families of wavelet paraunitary filters that converge almost everywhere to the Shannon paraunitary filters. From this analysis, we derive wavelet packet based spectrum estimation for fractional Brownian motions and wide-sense stationary random processes. Experimental tests show good results for estimating the spectrum of 1/f processes