Open AccessReweighted l 1 ‐norm minimisation for high‐resolution DOA estimation under unknown mutual coupling
Author(s) -
Meng Dandan,
Wang Xianpeng,
Huang Mengxing,
Shen Chong
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.6844
Subject(s) - minimisation (clinical trials) , algorithm , computer science , compressed sensing , inference , norm (philosophy) , sparse approximation , mathematics , pattern recognition (psychology) , artificial intelligence , statistics , political science , law
A reweighted l 1 ‐norm minimisation algorithm for high‐resolution direction‐of‐arrival (DOA) estimation under unknown mutual coupling is proposed in this Letter. First, the proposed method forms a new block representation model by parameterising the steering vector. Then, a reweighted l 1 ‐norm constraint based on the new data model is proposed, in which the principle of a novel multiple signals classification (MUSIC)‐like algorithm is used to construct a weighted matrix. Finally, the DOAs can be achieved by the recovered sparse matrix. Owing to the use of the whole received data and reweighted procedure, the performance of the proposed method is better than the state‐of‐the‐art methods. Extensive simulation experiments confirm that the above inference is correct.