基于物理信息深度神经网络的脉冲间时变振动补偿合成孔径雷达成像

IF 3.6 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Rongrong Wang , Jiongge Zhang , Jiarui Li , Long Tian , Junkun Yan , Bingnan Wang , Hongwei Liu
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引用次数: 0

摘要

合成孔径雷达(SAL)为遥感提供高分辨率、高数据速率和详细的成像。然而,短波长使得SAL系统对振动高度敏感,引入了多普勒频移和距离单元迁移,从而降低了图像质量,特别是对于扩展目标。传统的振动补偿方法在具有剧烈振动条件的复杂场景或缺乏强散射点时往往存在局限性。为了解决这些问题,首先建立了二阶振动误差模型来表征每个可调周期内的时变误差。然后,设计一个具有物理信息的深度神经网络,通过其编码器估计振动系数,然后由解码器中的物理层使用该系数来纠正误差。该方法将物理模型与数据驱动方法相结合,可以在不依赖强散射点的情况下减轻严重的振动误差,减少距离单元迁移。此外,物理层的集成使得解码器是非参数化的,从而简化了网络的训练。数值结果验证了该方法的有效性及其相对于传统光谱相关算法的优越性,显示了其在高分辨率SAL成像中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inter-pulse time-varying vibration compensation with a physically-informed deep neural network for synthetic aperture Ladar imaging
Synthetic Aperture Ladar (SAL) provides high-resolution, high-data-rate, and detailed imaging for remote sensing. However, its short wavelength makes SAL systems highly sensitive to vibrations, introducing Doppler frequency shifts and range cell migration that degrade image quality, particularly for extended targets. Traditional vibration compensation methods often face limitations in challenging scenes with severe vibration conditions or when strong scattering points are absent. To address these challenges, a second-order vibration error model is firstly developed to characterize the time-varying errors within each tunable period. Then, a physically-informed deep neural network is designed to estimate the vibration coefficients through its encoder, which are then used by physical layers in the decoder to correct the errors. By combining the physical model with a data-driven approach, the proposed method can mitigate severe vibration-induced errors and reduce range cell migration without relying on strong scattering points. Additionally, integration of the physical layers makes the decoder being non-parametric, thus simplifies the network training. Numerical results validate the method’s effectiveness and its superiority over traditional spectral correlation algorithm, demonstrating its potential for high-resolution SAL imaging.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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