Sidelobe Suppression Techniques for Near-Field Multistatic SAR
Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and freque...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/732 |
| _version_ | 1797437285388517376 |
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| author | George A. J. Price Chris Moate Daniel Andre Peter Yuen |
| author_facet | George A. J. Price Chris Moate Daniel Andre Peter Yuen |
| author_sort | George A. J. Price |
| collection | DOAJ |
| description | Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery. |
| first_indexed | 2024-03-09T11:17:57Z |
| format | Article |
| id | doaj.art-2abad5bd83c24782ab2ba85d3eee25a8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:57Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2abad5bd83c24782ab2ba85d3eee25a82023-12-01T00:26:38ZengMDPI AGSensors1424-82202023-01-0123273210.3390/s23020732Sidelobe Suppression Techniques for Near-Field Multistatic SARGeorge A. J. Price0Chris Moate1Daniel Andre2Peter Yuen3Radar & Electronic Warfare, QinetiQ, Malvern WR14 3PS, UKRadar & Electronic Warfare, QinetiQ, Malvern WR14 3PS, UKCentre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the United Kingdom, Shrivenham SN6 8LA, UKCentre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the United Kingdom, Shrivenham SN6 8LA, UKMultirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.https://www.mdpi.com/1424-8220/23/2/732SARmultistaticcompressive sensingUAS |
| spellingShingle | George A. J. Price Chris Moate Daniel Andre Peter Yuen Sidelobe Suppression Techniques for Near-Field Multistatic SAR Sensors SAR multistatic compressive sensing UAS |
| title | Sidelobe Suppression Techniques for Near-Field Multistatic SAR |
| title_full | Sidelobe Suppression Techniques for Near-Field Multistatic SAR |
| title_fullStr | Sidelobe Suppression Techniques for Near-Field Multistatic SAR |
| title_full_unstemmed | Sidelobe Suppression Techniques for Near-Field Multistatic SAR |
| title_short | Sidelobe Suppression Techniques for Near-Field Multistatic SAR |
| title_sort | sidelobe suppression techniques for near field multistatic sar |
| topic | SAR multistatic compressive sensing UAS |
| url | https://www.mdpi.com/1424-8220/23/2/732 |
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