Radar cross section (RCS) is a scattering measure of an object that scatters to the radar. However, existing methods for near-field (NF) measurement and data processing rarely extract amplitude characteristics, and there is a lack of effective verification of far-field (FF) data in the process of NF to FF transformation, which leads to inaccuracies in FF prediction accuracy. In this paper, we propose a method to establish the relationship between the NF and FF RCS using the state space method (SSM), which is based on accurate estimation of the NF amplitude in NF measurement, and then deriving the FF RCS from the NF scattering signal convolved with a near-to-far kernel. The proposed solution to address the uncertainty issue in reference FF data involves using the geometric theory of diffraction (GTD) scattering center model as the reference FF data and establishing a linear equation with the derived FF model. The negative gradient search (NGS) system identification concept is used to optimize the FF model in order to reduce the discrepancy between the reference and derived values. Finally, the corrected RCS error is provided as additional proof of the effectiveness of these techniques in enhancing near-to-far transformation accuracy by examining the outcomes of three experiments.
The signature extraction and processing are the key aspects of enhancing recognition ability for radar target characteristics. In this paper, to reveal the intrinsic property of the target and improve the accuracy of radar cross section(RCS), we proposed RCS diagnostic imaging technique, extracting the parameters of scattering center model by state space method(SSM), to analyze the signature of GTD-based returns. Comparison with estimating signal parameter via rotational invariance techniques(ESPRIT) method, reconstructing the RCS profile through original and recovery signal, and employing SSM to the PEC sphere simulation, both the signature of specular and creeping wave extraction, the validation of SSM completely achieved the diagnostic imaging by means of parameter extraction.
The signature extraction and processing are the key aspects of enhancing recognition ability for radar target characteristics. In this paper, to reveal the intrinsic property of the target and improve the accuracy of radar cross section (RCS), we proposed RCS diagnostic imaging technique, extracting the parameters of scattering center model by state space method (SSM), to analyze the signature of GTD‐based returns. To demonstrate the effectiveness of SSM, the comparison with estimating signal parameter via rotational invariance techniques method by the analysis of goodness‐of‐fit and root mean square error, reconstructing the RCS profile through original signal model, and employing SSM to extract the signature of specular and creeping wave from the PEC sphere. Furthermore, the RCS diagnostic imaging by SSM extracting position and amplitude is discussed, and range‐isolated pole technique specific to the creeping wave extraction is addressed the advantage of SSM.
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