Open AccessNovel subspace method for frequencies estimation of two sinusoids with applications to vital signals
Author(s) -
Chen YiSheng,
Lin YueDer
Publication year - 2017
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.2016.0702
Subject(s) - eigenvalues and eigenvectors , subspace topology , linear subspace , signal subspace , autocorrelation matrix , algorithm , computer science , interleaving , noise (video) , matrix (chemical analysis) , signal (programming language) , eigendecomposition of a matrix , set (abstract data type) , autocorrelation , mathematics , speech recognition , artificial intelligence , statistics , materials science , composite material , quantum mechanics , image (mathematics) , programming language , operating system , physics , geometry
This study proposes a novel subspace method to estimate frequencies of two sinusoids embedded in noise. The estimation process is basically composed of three main steps. Two principal eigenvectors of the autocorrelation matrix for the received vector are first found. Then, the authors use the elements of these eigenvectors to form a set of linear and non‐linear equations to solve an ambiguous matrix linked two signal subspaces which contain the information of frequencies. With the product of the matrix formed from these two eigenvectors and the solved ambiguous matrix, they can finally find the frequencies. In addition, they also combine an interleaving technique with the proposed method to improve the estimation performance for two close sinusoidal frequencies. Simulation results for synthetic data and practical vital signals are used to demonstrate the performance of the proposed method. The demonstrated results show that the proposed method is feasible for frequencies estimation of two sinusoids, and it can be applied to the case of very close sinusoidal frequencies.