Paper Info
Title
Matching pursuit for direction of arrival estimation in the presence of Gaussian noise and impulsive noise
Abstract
Two high-resolution direction of arrival (DOA) estimation approaches of non-stationary narrowband signals based on matching pursuit (MP) are developed. The first sensor output is considered as the reference and decomposed by MP. As the MP is a linear decomposition, the obtained MP coefficients contain the steering vector information. So, the MP coefficients corresponding to the leading decomposition atoms are used to develop the MP-MUSIC algorithm for the DOA estimation. In addition, the chosen MP atoms are used to implement the modified spatial time-frequency distribution (STFD) based on Wigner Ville (WV) distribution as well, and this method named MP-WV. It has been demonstrated that these two methods can be applied for underdetermined problems and are robust against Gaussian and impulsive noises. The authors show that using either coefficients or chosen atoms to estimate the DOA in array processing by considering the source discriminative capability outperforms the conventional MUSIC and STFD. Some simulation results showing the performance of the two proposed approaches based on MP, conventional MUSIC and STFD are presented.
Year
DOI
Venue
2014
10.1049/iet-spr.2013.0286
IET Signal Processing
Keywords
Field
DocType
gaussian noise,wigner distribution,array signal processing,direction-of-arrival estimation,impulse noise,signal classification,time-frequency analysis,doa estimation approaches,mp-music algorithm,stfd,wv distribution,wigner ville distribution,array processing,decomposition atoms,high-resolution direction of arrival estimation approaches,impulsive noise,linear decomposition,matching pursuit,modified spatial time-frequency distribution,source discriminative capability,steering vector information,time frequency analysis
Matching pursuit,Array processing,Narrowband,Underdetermined system,Control theory,Direction of arrival,Algorithm,Speech recognition,Gaussian,Discriminative model,Gaussian noise,Mathematics
Journal
Volume
Issue
ISSN
8
5
1751-9675
Citations 
PageRank 
References 
2
0.38
12
Authors
1
Name
Order
Citations
PageRank
S. Ghofrani1152.40