Open AccessGaussian mixture model approximation of total spatial power spectral density for multiple incoherently distributed sources
Author(s) -
Wang Huigang,
Li Shanlong,
Li Huxiong,
Shi Yang
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2011.0328
Subject(s) - gaussian , spectral density , covariance , mixture model , algorithm , gaussian process , mixture distribution , probability density function , mathematics , computer science , pattern recognition (psychology) , statistics , artificial intelligence , physics , quantum mechanics
Practically, the spatial power spectral density (PSD) of single or multiple incoherently distributed (ID) sources is often unknown, and the total spatial PSD is suitable to model the spatial distribution characteristic of signals if the number of multiple ID sources is also unknown. In this study, the Gaussian mixture model (GMM) is employed to characterise the total spatial PSD of multiple ID sources, and two algorithms are proposed to estimate the parameters of the GMM. The first one is the covariance fitting method for multiple ID sources with Gaussian PSD, and the other is the iterative expectation maximisation (EM) algorithm. Simulation studies demonstrate that the EM algorithm outperforms other methods in approximating the shape of the total spatial PSD, especially for small spatial spread.