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Rotating object model is commonly used for imaging analysis in high resolution radars such as the inverse synthetic aperture radar (ISAR). For a rotating object, it is known that multi-aspect observations can improve cross-range resolution with the known imaging geometry. For the non-cooperative rotating object with unknown imaging geometry, this paper proposes an integrated scheme to estimate the key parameters, e.g., the rotating velocity and the aspect angle difference between every two observations. Furthermore, convolution back-projection (CBP) method is applied to provide fused imaging result with improved resolution. Also, the accuracy of the ultimate parameter estimation is analyzed, which is strongly related with several important factors like position extraction error of scattering centers and so on. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed method.
Rotating object model is commonly used for imaging analysis in high resolution radars such as the inverse synthetic aperture radar (ISAR). For a rotating object, it is known that multi-aspect observations can improve cross-range resolution with the known imaging geometry. For the non-cooperative rotating object with unknown imaging geometry, this paper proposes an integrated scheme to estimate the key parameters, eg, the rotating velocity and the aspect angle difference between every two observations. to provide fused imaging result with improved resolution. Also, the accuracy of the ultimate parameter estimation is analyzed, which is strongly related with several important factors like position extraction error of scattering centers and so on. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed method.