Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise
Signal Processing - Signal processing with heavy-tailed models
On some detection and estimation problems in heavy-tailed noise
Signal Processing - Signal processing with heavy-tailed models
Capture Properties of the Generalized CMA in Alpha-Stable Noise Environment
Wireless Personal Communications: An International Journal
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
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New methods for parameter estimation and blind channel identification in impulsive signal environments are presented, where the signals/noise are modeled as symmetric α-stable (SαS) processes. First, we present methods for estimating the parameters (characteristic exponent α and dispersion γ) of a SαS distribution from a time series. The fractional lower order moments, with both positive and negative orders, and their applications to signal processing are introduced. Then we present a new algorithm for blind channel identification using the output fractional lower order moments, and the α-Spectrum, a new spectral representation for impulsive signals, is introduced. From the α-Spectrum, we establish the blind identifiability conditions of any FIR channel (mixed-phase, unknown order) with i.i.d. SαS (α>1) input. As a byproduct, a simple algorithm for recovering the phase of any type of a signal from the magnitude of its z-transform is presented. The novelty of our paper is in parameter estimation and blind identification of the FIR channel based on fractional lower order moments of its output data. Monte Carlo simulations clearly demonstrate the performance of the new methods