Title :
Bispectrum based estimation of parameters of Middleton Class A noise
Author :
Felix, V.P. ; Chander, V.
Author_Institution :
Naval. Phys. & Oceanographic Lab., Cochin, India
Abstract :
The proposed approach is applicable if the non-Gaussian component of the process has a non-zero mean and a skewed distribution. If the Gaussian background noise is white, it is shown that a linear relationship exists between bispectral magnitude on one frequency axis and the power spectral density of the process. Least squares estimates of the unknowns of the equation can then be obtained, from which the parameters of the process follow. Expressions for bispectrum of a stationary Poisson impulse process in terms of the rate of the process are derived. Asymptotic bias in the estimates of bispectrum of non-Gaussian processes in an additive Gaussian process is derived
Keywords :
least squares approximations; parameter estimation; random noise; spectral analysis; Gaussian background noise; Middleton Class A noise; bispectrum-based parameter estimation; least squares estimates; power spectral density; skewed distribution; stationary Poisson impulse process; Background noise; Frequency; Gaussian noise; Gaussian processes; Interference; Least squares approximation; Parameter estimation; Parametric statistics; Poisson equations; Working environment noise;
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
DOI :
10.1109/SSAP.1992.246824