Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of IS...Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of ISAR systems.Also,data scarcity poses a greater challenge to the accurate recognition of components.To address the issues of component recognition in complex ISAR targets,this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features.The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure.Then,the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes,thereby achieving significantly higher structural adaptability.Meanwhile,the scattering trait enhancement module and the statistical quantification module are designed.The edge texture is enhanced based on the scatter quantization property,which alleviates the segmentation challenge of edge blurring under low SNR conditions.The coupling of query/support samples is enhanced through four-dimensional convolution.Additionally,to overcome fusion challenges caused by information differences,multimodal feature fusion is guided by equilibrium comprehension loss.In this way,the performance potential of the fusion framework is fully unleashed,and the decision risk is effectively reduced.Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion,and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.展开更多
Exact estimation of space object attitude parameters is a great challenge.The effectiveness of conventional attitude estimation approaches based on target sizes suffers a significant reduction when occlusion exists.Th...Exact estimation of space object attitude parameters is a great challenge.The effectiveness of conventional attitude estimation approaches based on target sizes suffers a significant reduction when occlusion exists.This paper proposes an innovative approach to estimate the attitude parameters for space objects based on inverse synthetic aperture radar(ISAR)image sequences.The formulation for nonlinear size constraints(NSC)is developed by accounting for the characteristics of object size variation in ISAR image sequences.The multi-start framework for global optimization and the Broyden-Fletcher-Goldfarb-Shanno(BFGS)based quasi-Newton iterative method are combined with and used for more accurate estimation of space object’s attitude parameters.Furthermore,the Cramer-Rao lower bound(CRLB)of attitude parameter estimates is derived.Comparative experiments demonstrate the effectiveness and robustness of the proposed method.展开更多
文摘Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of ISAR systems.Also,data scarcity poses a greater challenge to the accurate recognition of components.To address the issues of component recognition in complex ISAR targets,this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features.The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure.Then,the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes,thereby achieving significantly higher structural adaptability.Meanwhile,the scattering trait enhancement module and the statistical quantification module are designed.The edge texture is enhanced based on the scatter quantization property,which alleviates the segmentation challenge of edge blurring under low SNR conditions.The coupling of query/support samples is enhanced through four-dimensional convolution.Additionally,to overcome fusion challenges caused by information differences,multimodal feature fusion is guided by equilibrium comprehension loss.In this way,the performance potential of the fusion framework is fully unleashed,and the decision risk is effectively reduced.Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion,and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.
文摘Exact estimation of space object attitude parameters is a great challenge.The effectiveness of conventional attitude estimation approaches based on target sizes suffers a significant reduction when occlusion exists.This paper proposes an innovative approach to estimate the attitude parameters for space objects based on inverse synthetic aperture radar(ISAR)image sequences.The formulation for nonlinear size constraints(NSC)is developed by accounting for the characteristics of object size variation in ISAR image sequences.The multi-start framework for global optimization and the Broyden-Fletcher-Goldfarb-Shanno(BFGS)based quasi-Newton iterative method are combined with and used for more accurate estimation of space object’s attitude parameters.Furthermore,the Cramer-Rao lower bound(CRLB)of attitude parameter estimates is derived.Comparative experiments demonstrate the effectiveness and robustness of the proposed method.
