Optimal Sensing Principle of Synthetic Aperture Radar

The article proposes a generalized optimal sensing principle for synthetic aperture radar (SAR) imaging, maximizing the mutual information between the sensed object and the reconstructed image with the optimal SAR measurement matrix. Inspired by Shannon's capacity theorem, the SAR sensing capacity is derived, which represents the maximum mutual information that can be acquired per unit distance or unit area in 1-D or 2-D scenarios. The SAR sensing capacity serves as a theoretical performance bound, guiding the design of SAR sensing systems and enabling reasonable estimation of systems' performance. Additionally, this article analyzes the relationship between system parameters and the column correlation of SAR measurement matrices, guiding the design of the SAR system. Furthermore, the optimal sensing principle is applied to the variable-resolution SAR (VR-SAR) imaging system. Theoretical simulations are conducted to verify the feasibility of the optimal sensing principle, and examine the relationship between column correlation and SAR parameters. Additionally, the advantages of VR-SAR based on the optimal sensing principle are compared with those of the conventional strip-map SAR mode.