Open AccessSinusoidal frequency estimation by multiple signal classification in frequency domain beam‐space
Author(s) -
Guo Wei,
Shen Guanghao,
Xie Kangning,
Wu Xiaoming,
Tang Chi,
Liu Juan,
Jia Min,
Jing Da,
Lei Tao,
Luo Erping
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2014.0246
Subject(s) - covariance , frequency domain , algorithm , mathematics , signal (programming language) , computer science , mean squared error , speech recognition , noise (video) , curse of dimensionality , statistics , artificial intelligence , computer vision , image (mathematics) , programming language
A novel method is presented to estimate sinusoidal frequency from highly contaminated single channel signals by constructing multi‐channel surrogates using multiple signal classification (MUSIC) method in frequency domain beam‐space (FB‐MUSIC). According to the comparability of sampled data in time domain and observed data in uniform linear array, the FB‐MUSIC method is proposed and the explicit expressions for the covariance elements of the estimation errors associated with FB‐MUSIC are derived. These expressions are then used to analyse the statistical performance of FB‐MUSIC and MUSIC. These expressions for the estimation error covariance are also used to compare the theoretical results and simulation results. Monte‐Carlo simulations show that the root‐mean‐square error of frequency estimation in simulations keep consistent with the theoretical covariance for FB‐MUSIC and MUSIC, and the signal‐to‐noise ratio resolution threshold of FB‐MUSIC with reduced dimensionality is lower than that of MUSIC. This method may provide a higher resolution of sinusoidal frequency estimation and lower computation cost as compared with the conventional MUSIC method.