An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays
The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array st...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/9/3176 |
| _version_ | 1797569049899565056 |
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| author | Jiancheng Liu Feng Shi Yecheng Sun Peng Li |
| author_facet | Jiancheng Liu Feng Shi Yecheng Sun Peng Li |
| author_sort | Jiancheng Liu |
| collection | DOAJ |
| description | The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance. |
| first_indexed | 2024-03-10T20:05:26Z |
| format | Article |
| id | doaj.art-74e2ccfead6b403e9639d9d526d7dd0c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:05:26Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-74e2ccfead6b403e9639d9d526d7dd0c2023-11-19T23:19:25ZengMDPI AGApplied Sciences2076-34172020-05-01109317610.3390/app10093176An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor ArraysJiancheng Liu0Feng Shi1Yecheng Sun2Peng Li3Collaborative Innovation Center of Atmospheric Environment and Equipment Technology in Jiangsu, Nanjing 210044, ChinaCollaborative Innovation Center of Atmospheric Environment and Equipment Technology in Jiangsu, Nanjing 210044, ChinaCollaborative Innovation Center of Atmospheric Environment and Equipment Technology in Jiangsu, Nanjing 210044, ChinaCollaborative Innovation Center of Atmospheric Environment and Equipment Technology in Jiangsu, Nanjing 210044, ChinaThe Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance.https://www.mdpi.com/2076-3417/10/9/3176underwatersonar imagingsensor arrayADSsparse optimization |
| spellingShingle | Jiancheng Liu Feng Shi Yecheng Sun Peng Li An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays Applied Sciences underwater sonar imaging sensor array ADS sparse optimization |
| title | An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays |
| title_full | An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays |
| title_fullStr | An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays |
| title_full_unstemmed | An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays |
| title_short | An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays |
| title_sort | ads based sparse optimization method for sonar imaging sensor arrays |
| topic | underwater sonar imaging sensor array ADS sparse optimization |
| url | https://www.mdpi.com/2076-3417/10/9/3176 |
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