An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection
The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak...
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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/1424-8220/18/12/4160 |
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| author | Peng Li Xinhua Zhang Lanrui Li Wenlong Zhang |
| author_facet | Peng Li Xinhua Zhang Lanrui Li Wenlong Zhang |
| author_sort | Peng Li |
| collection | DOAJ |
| description | The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak targets. Firstly, from the point of phase compensation, we analyze the cause of side lobes when IBF is applied. Then the improved IBF algorithm recorded as GIBF (the improved inverse beamforming) is proposed by changing the Toeplitz average into the phase construction. The theoretical derivation and simulation data processing show the proposed method can improve the resolution of the <i>N</i> sensors to the standard of 2<i>N</i> − 1 sensors under different signal-to-noise ratios. Compared with IBF, GIBF has great advantages in detecting weak targets. Passive sonar data are used to further verify the advantages of GIBF; the trajectories on azimuth history diagrams become clear, the azimuth resolution is improved, and the detection ability for weak targets is still robust. In addition, GIBF is combined with the common DOA (direction of arrival) estimation algorithms, such as conventional beamforming and minimum variance distortionless signal response, which proves the applicability of the algorithm. |
| first_indexed | 2024-04-13T08:18:45Z |
| format | Article |
| id | doaj.art-210c5045f6804f0c921058b9af8999ba |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:18:45Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-210c5045f6804f0c921058b9af8999ba2022-12-22T02:54:42ZengMDPI AGSensors1424-82202018-11-011812416010.3390/s18124160s18124160An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target DetectionPeng Li0Xinhua Zhang1Lanrui Li2Wenlong Zhang3Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaDepartment of Underwater Weaponry & Chemical Defense, Dalian Navy Academy, Dalian 116018, ChinaDepartment of Underwater Weaponry & Chemical Defense, Dalian Navy Academy, Dalian 116018, ChinaThe inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak targets. Firstly, from the point of phase compensation, we analyze the cause of side lobes when IBF is applied. Then the improved IBF algorithm recorded as GIBF (the improved inverse beamforming) is proposed by changing the Toeplitz average into the phase construction. The theoretical derivation and simulation data processing show the proposed method can improve the resolution of the <i>N</i> sensors to the standard of 2<i>N</i> − 1 sensors under different signal-to-noise ratios. Compared with IBF, GIBF has great advantages in detecting weak targets. Passive sonar data are used to further verify the advantages of GIBF; the trajectories on azimuth history diagrams become clear, the azimuth resolution is improved, and the detection ability for weak targets is still robust. In addition, GIBF is combined with the common DOA (direction of arrival) estimation algorithms, such as conventional beamforming and minimum variance distortionless signal response, which proves the applicability of the algorithm.https://www.mdpi.com/1424-8220/18/12/4160DOA estimationazimuth resolutionToeplitz averageinverse beamforming |
| spellingShingle | Peng Li Xinhua Zhang Lanrui Li Wenlong Zhang An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection Sensors DOA estimation azimuth resolution Toeplitz average inverse beamforming |
| title | An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection |
| title_full | An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection |
| title_fullStr | An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection |
| title_full_unstemmed | An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection |
| title_short | An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection |
| title_sort | improved inverse beamforming method azimuth resolution analysis for weak target detection |
| topic | DOA estimation azimuth resolution Toeplitz average inverse beamforming |
| url | https://www.mdpi.com/1424-8220/18/12/4160 |
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