LASSO Regression-Based DBF Technique for Waveform Decoupling of MIMO-SAR With Nonlinear Array Configuration

Waveform decoupling is usually considered a more technical challenge for fully exploiting the potential benefits provided by multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) structure. Spotlighted as a promising solution to this challenge, the well-known orthogonal-waveform beamforming scheme has become increasingly popular. However, in some cases, the performance of digital beamforming (DBF) involved in this scheme may be significantly degraded due to the nonlinear array configuration under stringent space constraints. Up until now, relatively little research on robust DBF on receive in elevation has been presented for a nonlinear array configuration. To alleviate this, we here propose a least absolute shrinkage and selection operator (LASSO) regression-based DBF technique for the improved segmented phase coding (SPC) decoupling scheme. First, a generalized steering vector formation model based on a 3-D geometric vector is provided for the subsequent DBF operation. Given that, due to the nonlinear array configuration, the calculated steering vector exhibits space-varying characteristics within the azimuth pulse extension of the illumination beam, steering vector constraints for the desired signal and interferences are subsequently designed to allow for such variation within the azimuth footprint. Furthermore, the beamforming problem is addressed by generalized LASSO regression to approach the goal that providing a distortionless response and deep nulls for each desired signal and interference component within the azimuth pulse extension. Finally, we assess the feasibility and performance of the proposed LASSO regression-based DBF technique for waveform decoupling with the use of numerical simulations.