Optimal Sensing Principle of Synthetic Aperture Radar Imaging System

The paper proposes an 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 2-D SAR sensing capacity is derived, which represents the maximum mutual information that can be acquired per unit area in 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. Furthermore, the optimal sensing principle is applied to the variable-resolution SAR (VR-SAR) imaging system. An equivalent experiment With Sentinel 1 Raw Data is conducted to verify the advantages of VR-SAR based on the optimal sensing principle.

1. INTRODUCTION

From the perspective of information theory, the synthetic aperture radar (SAR) imaging process can be viewed as acquiring information in the spatial domain [1]. In this analogy, the observed SAR image is treated as a received message carrying the target information, similar to how communication systems transmit information through modulation, typically in the time domain. Figure. 1 shows the analogous relationship between a communication system and a SAR sensing system. In SAR imaging, the information source is the scattering characteristics of the target scene. The scattering process can be considered a modulation of the transmitted waveform and the target scene. The received scattering amplitudes, after modulation by the transmitted waveforms, are demodulated to form SAR images, serving as the information sink in the information acquisition process.