Parameter estimation of a signal alongwith non-stationary non-Gaussian noise
A Mukherjee, A Sengupta - IECON 2007-33rd Annual …, 2007 - ieeexplore.ieee.org
IECON 2007-33rd Annual Conference of the IEEE Industrial …, 2007•ieeexplore.ieee.org
The problem of estimating the parameters of a one-dimensional signal comprising of a time
varying deterministic signal plus zero mean non-stationary non-Gaussian noise (NSNGN) is
addressed. An algorithm has been developed to estimate the deterministic signal as well as
the parameters of NSNGN of the signal. NSNGN has been modelled by Gaussian mixture
probability density functions (pdf). The parameters of NSNGN was estimated using
maximization of log likelihood function. Simulation results are included to validate this …
varying deterministic signal plus zero mean non-stationary non-Gaussian noise (NSNGN) is
addressed. An algorithm has been developed to estimate the deterministic signal as well as
the parameters of NSNGN of the signal. NSNGN has been modelled by Gaussian mixture
probability density functions (pdf). The parameters of NSNGN was estimated using
maximization of log likelihood function. Simulation results are included to validate this …
The problem of estimating the parameters of a one- dimensional signal comprising of a time varying deterministic signal plus zero mean non-stationary non-Gaussian noise (NSNGN) is addressed. An algorithm has been developed to estimate the deterministic signal as well as the parameters of NSNGN of the signal. NSNGN has been modelled by Gaussian mixture probability density functions (pdf). The parameters of NSNGN was estimated using maximization of log likelihood function. Simulation results are included to validate this algorithm.
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