Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng
{"title":"Joint sparsity-inducing DOA estimation for strictly noncircular sources with unknown mutual coupling","authors":"Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng","doi":"10.1109/SAM48682.2020.9104223","DOIUrl":null,"url":null,"abstract":"In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"46 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM48682.2020.9104223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.