MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one...

Area of Science:

• Radar Imaging
• Signal Processing
• Computational Electromagnetics

Background:

• Inverse synthetic aperture radar (ISAR) imaging often assumes targets are sparse.
• Existing 3D MIMO radar imaging algorithms can be improved for sparse signal recovery.

Purpose of the Study:

• To incorporate sparse signal recovery methods into 3D multiple-input multiple-output (MIMO) radar imaging.
• To develop a robust and accurate imaging algorithm for targets with sparse scattering characteristics.

Main Methods:

• Proposed a Sequential Order One Negative Exponential (SOONE) function to measure sparsity.
• Utilized gradient projection for nonconvex SOONE function minimization.
• Incorporated diagonal loading and singular value decomposition for improved robustness.
• Developed a combined amplitude and total-variation objective function for large flat surfaces.

Main Results:

• The gradient projection of SOONE function method outperformed several established sparse recovery algorithms in high SNR cases.
• Achieved superior simulated and real-data ISAR image quality compared to conventional methods.
• Demonstrated competitive performance against other advanced sparse signal recovery algorithms.

Conclusions:

• The proposed gradient projection of SOONE function method offers a robust and effective approach for 3D MIMO radar imaging.
• The method enhances the quality of ISAR images, particularly for targets with sparse scattering.
• The improvements in robustness and accuracy make it suitable for complex radar imaging applications.