An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays
Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4180 |
| _version_ | 1811306640459169792 |
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| author | Chenxi Guo Xinhong Hao Ping Li |
| author_facet | Chenxi Guo Xinhong Hao Ping Li |
| author_sort | Chenxi Guo |
| collection | DOAJ |
| description | Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri⁻Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks. |
| first_indexed | 2024-04-13T08:49:06Z |
| format | Article |
| id | doaj.art-a882c41203a5404fbb80cfe365b24a3a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:49:06Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a882c41203a5404fbb80cfe365b24a3a2022-12-22T02:53:33ZengMDPI AGSensors1424-82202018-11-011812418010.3390/s18124180s18124180An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar ArraysChenxi Guo0Xinhong Hao1Ping Li2National Key Laboratory of Mechatronic Engineering and Control, Beijing Institute of Technology, Beijing 100081, ChinaNational Key Laboratory of Mechatronic Engineering and Control, Beijing Institute of Technology, Beijing 100081, ChinaNational Key Laboratory of Mechatronic Engineering and Control, Beijing Institute of Technology, Beijing 100081, ChinaAngle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri⁻Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.https://www.mdpi.com/1424-8220/18/12/4180co-prime planar arrayimproved trilinear modelvirtual array conceptmatrix reconstruction |
| spellingShingle | Chenxi Guo Xinhong Hao Ping Li An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays Sensors co-prime planar array improved trilinear model virtual array concept matrix reconstruction |
| title | An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays |
| title_full | An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays |
| title_fullStr | An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays |
| title_full_unstemmed | An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays |
| title_short | An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays |
| title_sort | improved trilinear model based angle estimation method for co prime planar arrays |
| topic | co-prime planar array improved trilinear model virtual array concept matrix reconstruction |
| url | https://www.mdpi.com/1424-8220/18/12/4180 |
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