Reweighted l1‐norm minimisation for high‐resolution DOA estimation under unknown mutual coupling. Issue 23 (1st November 2018)

• Record Type: Journal Article
• Authors: Meng, Dandan; Wang, Xianpeng; Huang, Mengxing; Shen, Chong
• Is Part Of: Electronics letters. Volume 54:Issue 23(2018)
• Page Start: 1346
• Page End: 1348
• Publication Date: 2018-11-01
• Subjects: minimisation -- direction‐of‐arrival estimation -- vectors -- matrix algebra; recovered sparse matrix -- high‐resolution DOA estimation -- unknown mutual coupling -- high‐resolution direction‐of‐arrival estimation -- block representation model -- steering vector -- data model -- reweighted l1‐norm minimisation algorithm -- MUSIC‐like algorithm; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.6844
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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.