As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A ph...As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.展开更多
The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this...The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this problem,we deduce the echo model of the plasma-sheathenveloped reentry object.By estimating the coupled velocities,we propose a compensation method to correct the defocus of an inverse synthetic aperture radar(ISAR)image in range dimension to improve the quality of the ISAR images.The simulation results suggest that the echoes from different regions of the surface of the reentry object have various coupling velocities,and the higher the coupled velocity,the more serious the displacement and energy diffusion in the range dimension.Our proposed method can correct the range dimension aberration.Two measurement metrics were used to evaluate the improvement of the compensation method.展开更多
基金Supported by the National Natural Science Foundation of China(61071165)the Program for NewCentury Excellent Talents in University(NCET-09-0069)the Defense Industrial Technology Development Program(B2520110008)~~
文摘As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.
基金supported by National Natural Science Foundation of China(No.61971330)。
文摘The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this problem,we deduce the echo model of the plasma-sheathenveloped reentry object.By estimating the coupled velocities,we propose a compensation method to correct the defocus of an inverse synthetic aperture radar(ISAR)image in range dimension to improve the quality of the ISAR images.The simulation results suggest that the echoes from different regions of the surface of the reentry object have various coupling velocities,and the higher the coupled velocity,the more serious the displacement and energy diffusion in the range dimension.Our proposed method can correct the range dimension aberration.Two measurement metrics were used to evaluate the improvement of the compensation method.
基金supported in part by the Shanghai Aerospace Science and Technology Innovation Foundation(No.SAST 2021-026)the Fund of Prospec⁃tive Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics(NUAA).
文摘随着空间技术的飞速发展,空间态势感知能力需求不断增加。与传统光学传感器相比,逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)具有全天候、远距离高分辨率成像的能力,且成像不受光照条件的影响。此外,空间态势感知系统需要对周围航天器进行准确的评估,因此对空间目标部件识别能力的需求日益迫切。本文提出了一种基于YOLOv5结构的Multitask⁃YOLO网络,用于卫星ISAR图像中卫星帆板的识别和分割。首先,本文添加了分割解耦头来实现网络的分割功能。然后用空间金字塔池快速算法(Spatial pyramid pooling fast,SPPF)和距离交并比算法(Distance intersection over union,DIoU)代替原有结构,避免图像失真,加快收敛速度。通过在通道中引入注意机制,提高了分割和识别的准确性。最后使用模拟卫星的ISAR图像进行实验。结果表明,所提出的Multitask⁃YOLO网络高效、准确地实现了部件的识别和分割。与其他的识别和分割网络相比,该网络的平均精度(mean Average precision,mAP)和平均交并比(mean Intersection over union,mIoU)提高了约5%。此外,该网络的运行速度高达16.4 GFLOP,优于传统的多任务网络的性能。
