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Sparse recovery algorithm via adaptive grids for linear array SAR 3D sparse imaging
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AI
DOI：10.1080/01431161.2022.2157685.png)
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摘要
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Compressed sensing (CS) algorithms mostly achieve linear array synthetic aperture radar (LASAR) 3D sparse imaging under the On-Grid strategy, they suffer from the mismatch measurement matrix and decreased imaging quality due to the position-errors among targets and preset scattering units. Aimed at this problem, based on the Off-Grid strategy, a sparse recovery algorithm via adaptive grids (SRAG) is proposed in this paper. First, the proposed algorithm extracts the target scattering units in the imaging scene under the On-Grid strategy. Second, the target scattering units with position-errors are extracted under the non-zero adjacent scattering coefficient criterion, and they are defined as the unfocused scattering units. Third, according to whether the unfocused scattering units' distance is smaller than the scattering unit spacing, their search-areas of targets' real positions are set, respectively, and differently. Finally, based on the minimum scattering coefficient residual criterion, we adaptively compensate the unfocused scattering units' position-errors and re-estimate their scattering coefficients. Both simulation and experimental results indicate that the SRAG algorithm decreases the position-errors effectively, it improves the imaging quality, estimation accuracy of targets' positions, and scattering coefficients than traditional On-Grid CS algorithms.
Keyword:
LASAR
compressed Sensing
Off-Grid
mismatch measurement matrix
adaptive grids
期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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