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Signal Reconstruction Algorithm for Azimuth Multichannel SAR System Based on a Multiobjective Optimization Model.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Jun2020, Vol. 58 Issue 6, p3881-3893, 13p
- Publication Year :
- 2020
-
Abstract
- This article establishes a multiobjective optimization model to suppress the azimuth ambiguity power and noise simultaneously in signal reconstruction for a multichannel synthetic aperture radar (SAR) system. This multiobjective optimization model extends the theory of multichannel signal processing for reconstructing the SAR signal from the aliased signals. Linear scalarization and a quadratically constrained method for the multiobjective optimization model are applied to obtain $l_{1}$ norm optimization, $l_{2}$ norm optimization, and quadratically constrained optimization, respectively, in signal reconstruction. Azimuth ghosts can intuitively reflect the effects of azimuth ambiguity on SAR images. The $l_{1}$ norm optimization solution leads to a minimum upper bound of azimuth ghosts. A lowest azimuth ambiguity-to-signal ratio (AASR) can be derived by $l_{2}$ norm optimization. By relaxing the constraint of total ambiguity power suppression, one can obtain a minimum noise level in the case of quadratically constrained optimization. The reconstruction performances of the multiobjective optimization model in terms of AASR, signal-to-noise ratio (SNR), and signal-to-ambiguity-plus-noise ratio (SANR) are investigated with respect to the pulse repetition frequency (PRF) and compared with other methods for a multichannel SAR system. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 58
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 144948155
- Full Text :
- https://doi.org/10.1109/TGRS.2019.2959217