Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays
In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/11/21981 |
| _version_ | 1811301622473555968 |
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| author | Yujie Liang Rendong Ying Zhenqi Lu Peilin Liu |
| author_facet | Yujie Liang Rendong Ying Zhenqi Lu Peilin Liu |
| author_sort | Yujie Liang |
| collection | DOAJ |
| description | In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach. |
| first_indexed | 2024-04-13T07:12:09Z |
| format | Article |
| id | doaj.art-30d68c9327c049299977460c15de7afb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:12:09Z |
| publishDate | 2014-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-30d68c9327c049299977460c15de7afb2022-12-22T02:56:50ZengMDPI AGSensors1424-82202014-11-011411219812200010.3390/s141121981s141121981Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear ArraysYujie Liang0Rendong Ying1Zhenqi Lu2Peilin Liu3School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, ChinaIn the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.http://www.mdpi.com/1424-8220/14/11/21981off-gridjoint spatial sparsitydistributed sparse linear arraysdirection of arrival estimationconcatenated atomic normsemidefine programdistributed compressed sensing |
| spellingShingle | Yujie Liang Rendong Ying Zhenqi Lu Peilin Liu Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays Sensors off-grid joint spatial sparsity distributed sparse linear arrays direction of arrival estimation concatenated atomic norm semidefine program distributed compressed sensing |
| title | Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays |
| title_full | Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays |
| title_fullStr | Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays |
| title_full_unstemmed | Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays |
| title_short | Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays |
| title_sort | off grid direction of arrival estimation based on joint spatial sparsity for distributed sparse linear arrays |
| topic | off-grid joint spatial sparsity distributed sparse linear arrays direction of arrival estimation concatenated atomic norm semidefine program distributed compressed sensing |
| url | http://www.mdpi.com/1424-8220/14/11/21981 |
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