Signal Reconstruction Algorithm for Azimuth Multichannel SAR System Based on a Multiobjective Optimization Model

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.