Open AccessCovariance differencing‐based matrix decomposition for coherent sources localisation in bi‐static multiple‐input–multiple‐output radar
Author(s) -
Hong Sheng,
Wan Xianrong,
Cheng Feng,
Ke Hengyu
Publication year - 2015
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2014.0193
Subject(s) - toeplitz matrix , decorrelation , algorithm , covariance matrix , block (permutation group theory) , matrix (chemical analysis) , noise (video) , radar , mathematics , covariance , computer science , matrix decomposition , statistics , artificial intelligence , telecommunications , materials science , geometry , image (mathematics) , eigenvalues and eigenvectors , physics , quantum mechanics , pure mathematics , composite material
In this study, a covariance differencing‐based matrix decomposition algorithm is proposed for locating coherent sources under spatially coloured noise in bi‐static multiple‐input–multiple‐output (MIMO) radar. The method contains three steps. First, the covariance differencing technique is employed to eliminate sensor noise, especially the spatially coloured noise. Second, a block Toeplitz or block Hankel matrix is constructed for decorrelation with the covariance differenced matrix. The forward‐only, backward‐only and combined forward‐backward block Toeplitz/Hankel matrix constructions are defined, respectively. Third, unitary estimation of signal parameters by rotational invariance techniques (ESPRIT) algorithm is applied to estimate directions‐of‐departure (DODs) and directions‐of‐arrival (DOAs) of sources. The proposed algorithm offers several advantages. First, it is more robust and provides better estimation performance than other methods. Then, the coloured noise problem is overcome in a simple and effective way. Further, the computational load is comparatively low. Simulation results demonstrate the validity of the proposed algorithm.