Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling
Abstract In this paper, we address the joint estimation problem of elevation, azimuth, and polarization with nested array consists of complete six-component electromagnetic vector-sensors (EMVS). Taking advantage of the tensor permutation, we convert the sample covariance matrix of the receive data...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-07-01
|
| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-020-01764-8 |
| _version_ | 1818159667497402368 |
|---|---|
| author | Ming-Yang Cao Xingpeng Mao Lei Huang |
| author_facet | Ming-Yang Cao Xingpeng Mao Lei Huang |
| author_sort | Ming-Yang Cao |
| collection | DOAJ |
| description | Abstract In this paper, we address the joint estimation problem of elevation, azimuth, and polarization with nested array consists of complete six-component electromagnetic vector-sensors (EMVS). Taking advantage of the tensor permutation, we convert the sample covariance matrix of the receive data into a tensorial form which provides enhanced degree-of-freedom. Moreover, the parameter estimation issue with the proposed model boils down to a Vandermonde constraint Canonical Polyadic Decomposition problem. The structured least squares estimation of signal parameters via rotational invariance techniques is tailored for joint auto-pairing elevation, azimuth, and polarization estimation, ending up with a computational efficient method that avoids exhaustive searching over spatial and polarization region. Furthermore, the sufficient uniqueness analysis of our proposed approach is addressed, and the stochastic Cramér-Rao bound for underdetermined parameter estimation is derived. Simulation results are given to verify the effectiveness of the proposed method. |
| first_indexed | 2024-12-11T15:49:37Z |
| format | Article |
| id | doaj.art-35ea63e84d334948b137cec33f6294fb |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-11T15:49:37Z |
| publishDate | 2020-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-35ea63e84d334948b137cec33f6294fb2022-12-22T00:59:36ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-07-012020112310.1186/s13638-020-01764-8Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modelingMing-Yang Cao0Xingpeng Mao1Lei Huang2School of Electronics and Information Engineering, Harbin Institute of TechnologySchool of Electronics and Information Engineering, Harbin Institute of TechnologyCollege of Information Engineering, Shenzhen UniversityAbstract In this paper, we address the joint estimation problem of elevation, azimuth, and polarization with nested array consists of complete six-component electromagnetic vector-sensors (EMVS). Taking advantage of the tensor permutation, we convert the sample covariance matrix of the receive data into a tensorial form which provides enhanced degree-of-freedom. Moreover, the parameter estimation issue with the proposed model boils down to a Vandermonde constraint Canonical Polyadic Decomposition problem. The structured least squares estimation of signal parameters via rotational invariance techniques is tailored for joint auto-pairing elevation, azimuth, and polarization estimation, ending up with a computational efficient method that avoids exhaustive searching over spatial and polarization region. Furthermore, the sufficient uniqueness analysis of our proposed approach is addressed, and the stochastic Cramér-Rao bound for underdetermined parameter estimation is derived. Simulation results are given to verify the effectiveness of the proposed method.http://link.springer.com/article/10.1186/s13638-020-01764-8Electromagnetic vector-sensorNested arrayParameter estimationTensor decompositionCramér-Rao bound (CRB) |
| spellingShingle | Ming-Yang Cao Xingpeng Mao Lei Huang Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling EURASIP Journal on Wireless Communications and Networking Electromagnetic vector-sensor Nested array Parameter estimation Tensor decomposition Cramér-Rao bound (CRB) |
| title | Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling |
| title_full | Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling |
| title_fullStr | Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling |
| title_full_unstemmed | Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling |
| title_short | Elevation, azimuth, and polarization estimation with nested electromagnetic vector-sensor arrays via tensor modeling |
| title_sort | elevation azimuth and polarization estimation with nested electromagnetic vector sensor arrays via tensor modeling |
| topic | Electromagnetic vector-sensor Nested array Parameter estimation Tensor decomposition Cramér-Rao bound (CRB) |
| url | http://link.springer.com/article/10.1186/s13638-020-01764-8 |
| work_keys_str_mv | AT mingyangcao elevationazimuthandpolarizationestimationwithnestedelectromagneticvectorsensorarraysviatensormodeling AT xingpengmao elevationazimuthandpolarizationestimationwithnestedelectromagneticvectorsensorarraysviatensormodeling AT leihuang elevationazimuthandpolarizationestimationwithnestedelectromagneticvectorsensorarraysviatensormodeling |