Lei Fan;Qi Yang;Hongqiang Wang;Yuliang Qin;Bin Deng
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Considering the similarity of scene among inter-frame images, MTs can be detected based on target-level change detection after image registration and shadows left on the road. Following this, the defocused target region is transformed to obtain raw echoes, which is beneficial for parallel processing and reduce the computation amount. As for ISAR processing, the envelope alignment and auto-focus methods are employed to eliminate the residual motion errors and compensate for phase errors without constructing prior motion patterns. Thereafter, the ratio of equivalent rotational velocities between MTs and the scene is estimated to achieve the azimuth scaling. Finally, sparsity-based imaging enhancement is employed to further enhance the imaging quality. Simulations and airborne experiments are carried out to validate the effectiveness of the proposed method.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 9","pages":"8738-8753"},"PeriodicalIF":11.1000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sequential Ground Moving Target Imaging Based on Hybrid ViSAR-ISAR Image Formation in Terahertz Band\",\"authors\":\"Lei Fan;Qi Yang;Hongqiang Wang;Yuliang Qin;Bin Deng\",\"doi\":\"10.1109/TCSVT.2025.3556396\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sequential ground moving target imaging (GMTIm) is an imperative and challenging task under terahertz video synthetic aperture radar (THz-ViSAR), which contributes to fine-grained situational awareness and moving target (MT) recognition. 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引用次数: 0
摘要
时序地面运动目标成像(GMTIm)是太赫兹视频合成孔径雷达(THz-ViSAR)下一项必要且具有挑战性的任务,它有助于实现细粒度态势感知和运动目标识别。然而,传统的GMTIm方法通常是针对单帧图像设计的,这涉及到序列成像问题的重复参数估计,并且由于参数的敏感性而缺乏效率。针对上述问题,本文提出了一种基于太赫兹- visar -逆SAR (ISAR)混合成像的序列GMTIm方法。在ViSAR处理方面,首先获得序列成像结果。考虑到帧间图像之间场景的相似性,可以通过配准后的目标级变化检测和道路上留下的阴影来检测mt。然后对散焦目标区域进行变换,得到原始回波,有利于并行处理,减少计算量。在ISAR处理中,采用包络对准和自动对焦的方法消除残余运动误差和补偿相位误差,而无需构建先验运动模式。然后,估计mt与场景之间的等效旋转速度之比,实现方位角缩放。最后,采用基于稀疏度的成像增强,进一步提高成像质量。仿真和机载实验验证了该方法的有效性。
Sequential Ground Moving Target Imaging Based on Hybrid ViSAR-ISAR Image Formation in Terahertz Band
Sequential ground moving target imaging (GMTIm) is an imperative and challenging task under terahertz video synthetic aperture radar (THz-ViSAR), which contributes to fine-grained situational awareness and moving target (MT) recognition. However, traditional GMTIm methods are usually designed for single-frame images, which involves repetitive parameter estimation for the sequential imaging problem and lacks the efficiency due to the parameter sensitivity. To tackle the aforementioned problems, this paper proposes a sequential GMTIm method based on hybrid THz-ViSAR-inverse SAR (ISAR) image formation. With respect to ViSAR processing, the sequential imaging results are firstly obtained. Considering the similarity of scene among inter-frame images, MTs can be detected based on target-level change detection after image registration and shadows left on the road. Following this, the defocused target region is transformed to obtain raw echoes, which is beneficial for parallel processing and reduce the computation amount. As for ISAR processing, the envelope alignment and auto-focus methods are employed to eliminate the residual motion errors and compensate for phase errors without constructing prior motion patterns. Thereafter, the ratio of equivalent rotational velocities between MTs and the scene is estimated to achieve the azimuth scaling. Finally, sparsity-based imaging enhancement is employed to further enhance the imaging quality. Simulations and airborne experiments are carried out to validate the effectiveness of the proposed method.
期刊介绍:
The IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) is dedicated to covering all aspects of video technologies from a circuits and systems perspective. We encourage submissions of general, theoretical, and application-oriented papers related to image and video acquisition, representation, presentation, and display. Additionally, we welcome contributions in areas such as processing, filtering, and transforms; analysis and synthesis; learning and understanding; compression, transmission, communication, and networking; as well as storage, retrieval, indexing, and search. Furthermore, papers focusing on hardware and software design and implementation are highly valued. Join us in advancing the field of video technology through innovative research and insights.