Performance-Optimized SAR Raw Data Quantization: On-Board Implementation and Trade-Off Analysis

Synthetic Aperture Radar (SAR) represents nowadays a key technology in Earth Observation (EO), evolving its original capabilities into both large-scale monitoring of geophysical parameters and very high-resolution imaging with short revisit times. The increase in system performance and the wide range of application scenarios require significant efforts in the design of current and future SAR missions: one of the most critical on-board operations is the digitization of the received echoes, directly impacting the final image quality and, at the same time, limited by the available downlink capacity. State-of-the-art quantization methods, such as Block Adaptive Quantization (BAQ), offer a good trade-off between signal quality and overall complexity but lack adaptivity to the imaged scenario. This leads to different impacts of the quantization error on the final SAR image. As an evolution of BAQ, Performance-Optimized BAQ (PO-BAQ) is a recently proposed quantization method, which addresses this issue by employing variable quantization rates across the scene, targeting specific performance requirements in the final SAR image. In this paper, we present a feasibility study of variable bitrate allocation in a realistic SAR mission scenario: to ensure flexibility, we consider the bitrate allocation map (BRM) to be uploaded at commanding phase during each ground segment contact, individually tailoring the required performance quality for each acquisition. State-of-the-art uplink data rates are considered, and the complete performance evaluation after SAR processing is carried out using the experimental on-board processor developed within the SOPHOS Horizon 2020 project.