A Method for Near-Field to Far-Field RCS Prediction Based on Parameter Extraction and Identification

In this study, insufficiently fine distance-phase extraction by the state space method (SSM) was managed by the Cramér-Rao lower bound (CRLB) method to enhance the accuracy of radar cross section (RCS) predictions, especially through near-field-to-far-field transformations (NFFFTs). Specifically, SSM was first employed to extract the amplitude and scalar distance of the geometrical theory of diffraction (GTD) scattering center model (SCM) for reconstructing the far-field (FF) radiated signal. Second, the CRLB was applied to increase the precision of distance extraction from GTD signals for refining FF echoes and obtaining vital distance information for precise localization of FF events. Third, the derived characteristics were systematically used to strengthen the quality of backscattered signals and hence estimate FF RCS values with parameter identification (PI) techniques. These advances bolster the predictive capacities of RCS within the purview of NFFFT, which comprises a major feature of this research. Furthermore, an in-depth review of PI methodologies was performed to improve the accuracy and precision of RCS predictions. Simulation results provide the underlying platform for later physical testing. Finally, the actual tests were conducted within a hexahedral anechoic chamber, demonstrating the efficiency of the proposed CRLB-based phase extraction and PI approaches in anticipating FF RCS values.