Optimal algorithm of SAR raw data processing for radar cross section estimation

Synthetic aperture radar (SAR) systems are pivotal in remote sensing, offering high-resolution imaging capabilities under diverse environmental conditions. This study introduces an optimal algorithm for SAR raw data processing, addressing the stochastic nature of surface scattering. By modeling the complex scattering coefficient as a random spatial process it is propose a statistically optimized signal processing framework. A key innovation is the incorporation of a decorrelation operation, which increases statistically independent samples, mitigates speckle noise, and enhances image quality. Simulation results demonstrate superior performance over conventional methods across multiple image quality metrics. The proposed algorithm improves radar cross section estimation, offering enhanced resolution and clarity for complex surfaces. This advancement holds significant potential for applications in Earth observation, geohazard monitoring, and structural analysis. Keywords. synthetic aperture radar, stochastic scattering, decorrelation processing, speckle noise reduction, radar cross section This is an archival copy of the original publication in CEUR Workshop Proceedings of the 2nd International Workshop on Computational Methods in Systems Engineering 2025 (CMSE 2025), vol. 3981, 2025, pp. 170-181 . Available online at https://ceur-ws.org/Vol-3981/paper15.pdf. Published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).